def __init__(self,
prophet_interval_width=0.95,
yearly_seasonality=False,
monthly_seasonality=False,
quarterly_seasonality=False,
weekly_seasonality=False,
daily_seasonality=False,
weekend_seasonality=False,
changepoint_prior_scale=0.001,
changepoint_range=0.9,
add_change_points=True,
diagnose=False,
history=None,
step=None,
horizon=None,
consider_holidays=True,
country='DE',
**kwds):
"""Initializes the object ProphetForecaster"""
self._prophet_logger = Logger('prophet')
try:
super(ProphetForecaster, self).__init__(**kwds)
except TypeError:
self._prophet_logger.exception(
"TypeError occurred, Arguments missing")
self._model = None
self._prophet_interval_width = prophet_interval_width
self._yearly_seasonality = yearly_seasonality
self._monthly_seasonality = monthly_seasonality
self._quarterly_seasonality = quarterly_seasonality
self._weekly_seasonality = weekly_seasonality
self._daily_seasonality = daily_seasonality
self._weekend_seasonality = weekend_seasonality
self._changepoint_prior_scale = changepoint_prior_scale
self._changepoint_range = changepoint_range
self._add_change_points = add_change_points
self._diagnose = diagnose
self._history = history
self._step = step
self._horizon = horizon
self._prophet_cv = None
self._prophet_p = None
self._consider_holidays = consider_holidays
self._country = country
self._id = 'Prophet'
def __init__(self,
start_p=1,
start_q=1,
max_p=3,
max_q=3,
d=None,
D=None,
start_P=1,
start_Q=1,
max_P=3,
max_Q=3,
random=False,
n_fits=10,
stepwise=True,
information_criterion='aic',
scoring='mse',
out_of_sample_size=0,
**kwds):
"""Initializes the object AutoARIMAForecaster"""
self._aarima_logger = Logger("AutoARIMA")
self._aarima_seasonal = False
self._aarima_trend = 'c'
self._start_p = start_p
self._start_q = start_q
self._max_p = max_p
self._max_q = max_q
self._d = d
self._D = D
self._start_P = start_P
self._start_Q = start_Q
self._max_P = max_P
self._max_Q = max_Q
self._random = random
self._n_fits = n_fits
self._stepwise = stepwise
self._information_criterion = information_criterion
self._scoring = scoring
self._out_of_sample_size = out_of_sample_size
try:
super(AutoARIMAForecaster, self).__init__(**kwds)
except TypeError:
self._aarima_logger.exception("Arguments missing...")
AutoARIMAForecaster._init_trend(self)
AutoARIMAForecaster._init_seasonal(self)
AutoARIMAForecaster.assertions(self)
self._id = 'Auto_ARIMA'
def __init__(self, **kwargs):
"""Initializes GridSearch class"""
self._gs_logger = Logger("grid_search")
self.forecaster = None
self.hyper_params = None
for k, v in kwargs.items():
if k == 'forecaster':
self.forecaster = v
elif k == 'hyper_params':
self.hyper_params = v
self.assertions()
self.results = list()
self.best_model = dict()
def __init__(self, s_order=(1, 0, 1, 1), **kwds):
"""Initializes the object SARIMAForecaster"""
self._sarima_logger = Logger("SARIMA")
self._s_order = s_order
self._sarima_trend = ''
try:
super(SARIMAForecaster, self).__init__(**kwds)
except TypeError as e:
self._sarima_logger.exception("Arguments missing...{}".format(e))
self._model = None
self._init_trend()
self.assertions()
self._id = 'SARIMA'
def __init__(self,
dlm_trend=None,
dlm_seasonality=None,
dlm_dynamic=None,
dlm_auto_reg=None,
dlm_long_season=None,
use_rolling_window=False,
window_size=0,
dlm_interval_width=0.95,
**kwds):
"""Initializes the object DLMForecaster"""
if dlm_trend is None:
dlm_trend = {
'degree': 0,
'discount': 0.99,
'name': 'trend1',
'w': 1e7
}
self._model = None
self.mse = None
self._dlm_trend = dlm_trend
self._dlm_seasonality = dlm_seasonality
self._dlm_dynamic = dlm_dynamic
self._dlm_auto_reg = dlm_auto_reg
self._dlm_long_season = dlm_long_season
self._use_rolling_window = use_rolling_window
self._window_size = window_size
self._dlm_interval_width = dlm_interval_width
self._dlm_logger = Logger('dlm')
self.assertions()
try:
super(DLMForecaster, self).__init__(**kwds)
except TypeError:
self._dlm_logger.exception("TypeError occurred, Arguments missing")
self._id = 'DLM'
self._train_dlm_dynamic = None # features
self._test_dlm_dynamic = None # featureDict
self._val_dlm_dynamic = None # featureDict
def __init__(self,
smoothing_level=None,
optimized=False,
damped=False,
smoothing_slope=None,
smoothing_seasonal=None,
damping_slope=None,
use_boxcox=False,
remove_bias=False,
use_brute=False,
**kwds):
"""Initializes the object ExponentialSmoothingForecaster"""
self._expsm_logger = Logger("ExpSmoothing")
self._es_trend = None
self._es_seasonal = None
try:
super(ExponentialSmoothingForecaster, self).__init__(**kwds)
except TypeError:
self._expsm_logger.exception("Arguments missing...")
self._init_trend()
self._init_seasonal()
self._smoothing_level = smoothing_level
self._optimized = optimized
self._damped = damped
self._smoothing_slope = smoothing_slope
self._smoothing_seasonal = smoothing_seasonal
self._damping_slope = damping_slope
self._use_boxcox = use_boxcox
self._remove_bias = remove_bias
self._use_brute = use_brute
self.assertions()
self._id = 'ExponentialSmoothing'
def __init__(self,
fit_intercept=True,
normalize=False,
copy_X=False,
n_jobs=None,
**kwds):
"""Initializes the object LinearForecaster"""
self._lin_logger = Logger('linear')
try:
super(LinearForecaster, self).__init__(**kwds)
except TypeError:
self._lin_logger.exception("Arguments missing...")
self._fit_intercept = fit_intercept
self._normalize = normalize
self._copy_X = copy_X
self._n_jobs = n_jobs
self.intercept = None
self.slope = None
self._id = 'Linear'
def __init__(self,
order=(1, 0, 1),
**kwds):
"""Initializes the object ARIMAForecaster"""
self._arima_logger = Logger("ARIMA")
self._order = order
self._arima_trend = ''
try:
super(ARIMAForecaster, self).__init__(**kwds)
except TypeError as e:
self._arima_logger.exception("Arguments missing...{}".format(e))
self._model = None
ARIMAForecaster._init_trend(self)
self._ar_coef = None
self._ma_coef = None
ARIMAForecaster.assertions(self)
self._id = 'ARIMA'
class UVariateTimeSeriesForecaster(LinearForecaster, AutoARIMAForecaster,
SARIMAForecaster,
ExponentialSmoothingForecaster,
ProphetForecaster, DLMForecaster):
"""Univariate time series class inheriting from all existing forecasters and choosing the best forecaster.
Attributes
----------
forecasters: list
List of all forecasters to be used. The best one will be chosen as the final model.
The goodness of measure is rmse of a model on test data.
Note, that it is necessary to generate the test data.
_dict_models: dictionary
Dictionary keeping all models
best_model: Object
The best model
_uvtsf_logger: Logger
The logger for logging
Methods
----------
assertions()
Assertion tests, must be overrided
ts_fit()
Fits all forecasters to time series
ts_test()
Test all forecasters on test data and computes rmse
_select_best()
Helper function to select the best model
select_best()
Fits all forecasters to time series and selects the best one based on rmse of each computed on test data.
If these was no test data, no test is done and no model is selected
plot_residuals()
Plots residuals for the best model
ts_forecast()
Forecasts time series and plots the results using the best model
plot_forecasts()
Plots forecasted time-series
"""
def __init__(self, forecasters=['all'], **kwds):
"""Initialized the object UVariateTimeSeriesForecaster"""
self.forecasters = list(map(lambda x: x.lower(), forecasters))
self._dict_models = dict() # .fromkeys(self._model_list, None)
self.best_model = None
self._uvtsf_logger = Logger("uvtsf")
#
try:
super(UVariateTimeSeriesForecaster, self).__init__(**kwds)
except TypeError:
self._uvtsf_logger.exception("Arguments missing...")
self._id = 'ts_forecaster'
#
if 'prophet' in self.forecasters:
self._dict_models['prophet'] = self.__copy__()
self._dict_models['prophet'].__class__ = ProphetForecaster
if 'linear' in self.forecasters:
self._dict_models['linear'] = self.__copy__()
self._dict_models['linear'].__class__ = LinearForecaster
if 'arima' in self.forecasters:
self._dict_models['arima'] = self.__copy__()
self._dict_models['arima'].__class__ = ARIMAForecaster
if 'sarima' in self.forecasters:
self._dict_models['sarima'] = self.__copy__()
self._dict_models['sarima'].__class__ = SARIMAForecaster
if 'auto_arima' in self.forecasters:
self._dict_models['auto_arima'] = self.__copy__()
self._dict_models['auto_arima'].__class__ = AutoARIMAForecaster
if 'exponential smoothing' in self.forecasters:
self._dict_models['expsm'] = self.__copy__()
self._dict_models[
'expsm'].__class__ = ExponentialSmoothingForecaster
if 'dlm' in self.forecasters:
self._dict_models['dlm'] = self.__copy__()
self._dict_models['dlm'].__class__ = DLMForecaster
if 'all' in self.forecasters:
self._dict_models['prophet'] = self.__copy__()
self._dict_models['prophet'].__class__ = ProphetForecaster
self._dict_models['linear'] = self.__copy__()
self._dict_models['linear'].__class__ = LinearForecaster
self._dict_models['arima'] = self.__copy__()
self._dict_models['arima'].__class__ = ARIMAForecaster
self._dict_models['sarima'] = self.__copy__()
self._dict_models['sarima'].__class__ = SARIMAForecaster
self._dict_models['auto_arima'] = self.__copy__()
self._dict_models['auto_arima'].__class__ = SARIMAForecaster
self._dict_models['expsm'] = self.__copy__()
self._dict_models[
'expsm'].__class__ = ExponentialSmoothingForecaster
self._dict_models['dlm'] = self.__copy__()
self._dict_models['dlm'].__class__ = DLMForecaster
self.assertions()
def __copy__(self):
"""Copies the object"""
result = super(UVariateTimeSeriesForecaster, self).__copy__()
#
result.forecasters = self.forecasters
result._dict_models = self._dict_models
result.best_model = self.best_model
return result
def assertions(self):
try:
assert isinstance(self.forecasters, list)
except AssertionError:
self._uvtsf_logger.exception(
"Assertion exception occurred, list expected for forecasters")
sys.exit("STOP")
for k, v in self._dict_models.items():
try:
assert self._dict_models[k].n_test > 0
except AssertionError:
self._uvtsf_logger.exception(
"Assertion exception occurred, no test data was generated! "
"This forecaster requires the test data")
sys.exit("STOP")
def ts_fit(self, suppress=False):
"""Fit all forecasters to the time series data.
Parameters:
----------
suppress: bool
Suppress or not some of the output messages
"""
for k, v in self._dict_models.items():
if self._dict_models[k] is not None:
self._dict_models[k].ts_fit(suppress=suppress)
return self
def ts_diagnose(self):
"""Diagnoses all candidate models"""
for k, v in self._dict_models.items():
if self._dict_models[k].model_fit is not None:
self._dict_models[k].ts_diagnose()
def ts_test(self, show_plot=True):
"""Test the fitted model if test data available"""
for k, v in self._dict_models.items():
if self._dict_models[k].model_fit is not None:
self._dict_models[k].ts_test(show_plot=show_plot)
self._select_best()
def _select_best(self):
"""Helper function to select the best model among fitted forecasters"""
rmse = float('Inf')
for k, v in self._dict_models.items():
if self._dict_models[k].model_fit is not None:
if self._dict_models[k].rmse < rmse:
rmse = self._dict_models[k].rmse
self.best_model = self._dict_models[k]
if self.best_model is not None:
self._uvtsf_logger.info("The best model selected as: {}".format(
str(type(self.best_model)).split('\'')[1].split('.')[2]))
else:
self._uvtsf_logger.warning(
"No model has been fitted! Please call ts_fit()...")
"""
def select_best(self, suppress=False):
Fit all forecasters and select the best model
self.ts_fit(suppress=suppress)
self.ts_test()
return self
"""
def plot_residuals(self):
"""Residual plots"""
if self.best_model is not None and bool(self.best_model):
self.best_model.plot_residuals()
else:
for k, v in self._dict_models.items():
if self._dict_models[k].model_fit is not None:
self._dict_models[k].plot_residuals()
def ts_validate(self, suppress=True, show_plot=True):
"""Validates the best model"""
if self.best_model is not None:
self.best_model.ts_validate(suppress=suppress, show_plot=show_plot)
else:
self._uvts_cls_logger.warning(
"No model has been selected yet! Run ts_test() first, or restart."
)
sys.exit("STOP")
def ts_forecast(self, n_forecast, suppress=False):
"""Forecast n_forecast steps in the future using the best model"""
if self.best_model is not None:
self.best_model.ts_forecast(n_forecast=n_forecast,
suppress=suppress)
else:
self._uvtsf_logger.warning(
"No model has been selected! Please call ts_test()...")
return self
def plot_forecast(self):
"""Plots forecasted values"""
if self.best_model is not None:
self.best_model.plot_forecast()
else:
self._uvtsf_logger.warning(
"No model has been selected! Please call ts_fit()...")
def __init__(self,
ts_df,
time_format="%Y-%m-%d %H:%M:%S",
freq='D',
fill_method='ffill',
n_test=0,
n_val=0,
hyper_params=None,
test='adf',
trend=None,
seasonal=False,
seasonal_periods=1,
**kwds):
"""Initializes the object UVariateTimeSeriesForecaster"""
self._ts_df_cols = ['ds', 'y']
self.ts_df = ts_df
self.time_format = time_format
self.freq = freq
self.fill_method = fill_method.lower()
self.n_test = int(n_test)
self.n_val = int(n_val)
self.transform = None
self._boxcox_lmbda = None
self._mode = ''
self._train_dt = None
self._test_dt = None
self._val_dt = None
self.model_fit = None
self.fittedvalues = None
self.residuals = None
self.rmse = 0
self._gs = tsa.GridSearchClass()
self.hyper_params = hyper_params
self.best_model = dict()
"""
self.rmse_test = 0
self.rmse_val = 0
"""
self.upper_whisker_res = None
self.lower_conf_int = None
self.upper_conf_int = None
self.forecast = None
self.residuals_forecast = None
self._res_decomp = None
self._arr_seasonal = None
self._arr_trend = None
self._arr_baseline = None
self._test = test
self._trend = trend
if self._trend is not None:
self._trend = self._trend.lower()
self._seasonal = seasonal
if isinstance(self._seasonal, str):
self._seasonal = self._seasonal.lower()
self._seasonal_periods = seasonal_periods
self._uvts_cls_logger = Logger('uvts_cls')
UVariateTimeSeriesClass.assertions(self)
# work with ts_df
self.ts_df = self.ts_df.reset_index()
self.ts_df.columns = self._ts_df_cols
self.ts_df['y'] = self.ts_df['y'].apply(np.float64, errors='coerce')
self.ts_df.set_index('ds', inplace=True)
self._uvts_cls_logger.info("Received time series data of range: " +
str(min(self.ts_df.index)) + ' - ' +
str(max(self.ts_df.index)) +
" and shape: " + str(self.ts_df.shape))
if not isinstance(self.ts_df.index, pd.DatetimeIndex):
self._uvts_cls_logger.warning("Time conversion required...")
self.ts_df = self.ts_df.reset_index()
try:
self.ts_df['ds'] = self.ts_df['ds'].apply(
lambda x: datetime.datetime.strptime(
str(x).translate({
ord('T'): ' ',
ord('Z'): None
})[:-1], self.time_format))
except ValueError as e:
self._uvts_cls_logger.warning(
"Zulu time conversion not successful: {}".format(e))
self._uvts_cls_logger.warning(
"Will try without assuming zulu time...")
try:
self.ts_df['ds'] = self.ts_df['ds'].apply(
lambda x: datetime.datetime.strptime(
str(x), self.time_format))
except ValueError as e:
self._uvts_cls_logger.info(
"Time conversion not successful. Check your time_format: {}"
.format(e))
sys.exit("STOP")
else:
self._uvts_cls_logger.info("Time conversion successful!")
else:
self._uvts_cls_logger.info("Time conversion successful!")
# set index
self.ts_df.set_index('ds', inplace=True)
#
self.ts_df.index = pd.to_datetime(self.ts_df.index)
self.ts_df.sort_index(inplace=True)
# resample
self.ts_resample()
UVariateTimeSeriesClass.assertions(self, post=True)
#
if self.n_val > len(self.ts_df) - self.n_test:
self.n_val = len(self.ts_df) - self.n_test
if self.n_test == 0 and self.n_val == 0:
self._mode = 'forecast'
elif self.n_test > 0:
self._mode = 'test'
elif self.n_test == 0 and self.n_val > 0:
self._mode = 'validate'
# delegate just for good programming style here
super(UVariateTimeSeriesClass, self).__init__(**kwds)
class DLMForecaster(UVariateTimeSeriesClass):
"""Univariate time series child class using DLM of pydlm for forecasting,
ref. to https://pydlm.github.io/pydlm_user_guide.html
Attributes
----------
_dlm_trend: tuple
A tuple of degree, discount, name and prior covariance
_dlm_seasonality: tuple
A tuple of period, discount, name and prior covariance
_dlm_dynamic: dictionary
A dictionary of tuples as features, discount, name and prior covariance.
Note, the features for _dynamic should be a list of lists.
_dlm_auto_reg: tuple
A tuple of degree, discount, name and prior covariance
_dlm_long_season: tuple
A tuple of period, stay, name and prior covariance
_use_rolling_window: bool
Use rolling window in forward filtering yes/no
_window_size: int
_dlm_interval_width: float
TBD
_dlm_logger: Logger
The logger for logging
Methods
----------
assertions()
Assertion tests, must be overrided
set_params()
Sets new parameter values
get_params_dict()
Gets parameter values as a dictionary
ts_fit()
Fits the auto_arima model to time series
ts_diagnose()
Diagnoses the fitted model
plot_dlm()
Plot pydlm native plots
plot_residuals()
Generates residual plots
ts_test()
Evaluates fitted model on the test data, if this one has been generated
ts_forecast()
Forecasts time series and plots the results
plot_forecasts()
Plots forecasted time-series
"""
def __init__(self,
dlm_trend=None,
dlm_seasonality=None,
dlm_dynamic=None,
dlm_auto_reg=None,
dlm_long_season=None,
use_rolling_window=False,
window_size=0,
dlm_interval_width=0.95,
**kwds):
"""Initializes the object DLMForecaster"""
if dlm_trend is None:
dlm_trend = {
'degree': 0,
'discount': 0.99,
'name': 'trend1',
'w': 1e7
}
self._model = None
self.mse = None
self._dlm_trend = dlm_trend
self._dlm_seasonality = dlm_seasonality
self._dlm_dynamic = dlm_dynamic
self._dlm_auto_reg = dlm_auto_reg
self._dlm_long_season = dlm_long_season
self._use_rolling_window = use_rolling_window
self._window_size = window_size
self._dlm_interval_width = dlm_interval_width
self._dlm_logger = Logger('dlm')
self.assertions()
try:
super(DLMForecaster, self).__init__(**kwds)
except TypeError:
self._dlm_logger.exception("TypeError occurred, Arguments missing")
self._id = 'DLM'
self._train_dlm_dynamic = None # features
self._test_dlm_dynamic = None # featureDict
self._val_dlm_dynamic = None # featureDict
def assertions(self):
if self._dlm_trend is not None:
try:
assert isinstance(self._dlm_trend, dict)
except AssertionError:
self._dlm_logger.exception(
"Assertion exception occurred, dictionary expected for dlm_trend"
)
sys.exit("STOP")
else:
len_keys = list(
filter(lambda x: x in list(self._dlm_trend.keys()),
keys_f(keys=['degree', 'discount', 'name'])))
try:
assert len(len_keys) == len(['degree', 'discount', 'name'])
except AssertionError:
self._dlm_logger.exception(
"Not all expected parameters found for trend. "
"['degree', 'discount', 'name'] are necessary!")
sys.exit("STOP")
else:
if 'w' not in list(self._dlm_trend.keys()):
self._dlm_trend['w'] = 1e7
if self._dlm_seasonality is not None:
try:
assert isinstance(self._dlm_seasonality, dict)
except AssertionError:
self._dlm_logger.exception(
"Assertion exception occurred, dictionary expected for dlm_seasonality"
)
sys.exit("STOP")
else:
len_keys = list(
filter(lambda x: x in list(self._dlm_seasonality.keys()),
keys_f(keys=['period', 'discount', 'name'])))
try:
assert len(len_keys) == len(['period', 'discount', 'name'])
except AssertionError:
self._dlm_logger.exception(
"Not all expected parameters found for seasonality. "
"['period', 'discount', 'name] are necessary!")
sys.exit("STOP")
else:
if 'w' not in list(self._dlm_seasonality.keys()):
self._dlm_seasonality['w'] = 1e7
if self._dlm_auto_reg is not None:
try:
assert isinstance(self._dlm_auto_reg, dict)
except AssertionError:
self._dlm_logger.exception(
"Assertion exception occurred, dictionary expected for dlm_auroReg"
)
sys.exit("STOP")
else:
len_keys = list(
filter(lambda x: x in list(self._dlm_auto_reg.keys()),
keys_f(keys=['degree', 'discount', 'name'])))
try:
assert len(len_keys) == len(['degree', 'discount', 'name'])
except AssertionError:
self._dlm_logger.exception(
"Not all expected parameters found for auto_reg. "
"['degree', 'discount', 'name'] are necessary!")
sys.exit("STOP")
else:
if 'w' not in list(self._dlm_auto_reg.keys()):
self._dlm_auto_reg['w'] = 1e7
if self._dlm_long_season is not None:
try:
assert isinstance(self._dlm_long_season, dict)
except AssertionError:
self._dlm_logger.exception(
"Assertion exception occurred, dictionary expected for dlm_longSeason"
)
sys.exit("STOP")
else:
len_keys = list(
filter(lambda x: x in list(self._dlm_long_season.keys()),
keys_f(keys=['period', 'stay', 'name'])))
try:
assert len(len_keys) == len(['period', 'stay', 'name'])
except AssertionError:
self._dlm_logger.exception(
"Not all expected parameters found for long season. "
"['period', 'stay', 'name'] are necessary!")
sys.exit("STOP")
else:
if 'w' not in list(self._dlm_long_season.keys()):
self._dlm_long_season['w'] = 1e7
if self._dlm_dynamic is not None:
try:
assert isinstance(self._dlm_dynamic, dict)
except AssertionError:
self._dlm_logger.exception(
"Assertion exception occurred, dictionary expected for dlm_seasonality"
)
sys.exit("STOP")
else:
try:
assert 'features' in list(self._dlm_dynamic.keys())
except AssertionError:
self._dlm_logger.exception(
"Assertion exception occurred, 'features' must be provided!"
)
sys.exit("STOP")
else:
try:
assert isinstance(self._dlm_dynamic['features'], list)
except AssertionError:
self._dlm_logger.exception(
"Assertion exception occurred, list expected for 'features'"
)
sys.exit("STOP")
else:
for i in range(len(self._dlm_dynamic['features'])):
len_keys = list(
filter(
lambda x: x in list(self._dlm_dynamic[
'features'][i].keys()),
keys_f(
keys=['features', 'discount', 'name'
])))
try:
assert len(len_keys) == len(
['features', 'discount', 'name'])
except AssertionError:
self._dlm_logger.exception(
"Not all expected parameters found for dynamic features. "
"['features', 'discount', 'name'] are necessary!"
)
sys.exit("STOP")
# features must have same length with the data
for i in range(len(self._dlm_dynamic['features'])):
try:
assert len(self._dlm_dynamic['features'][i]
['features']) == len(self.ts_df)
except AssertionError:
self._dlm_logger.exception(
"Assertion exception occurred. All provided features must"
" be of same length as your data!")
sys.exit("STOP")
else:
if 'w' not in list(
self._dlm_dynamic['features'][i].keys()):
self._dlm_dynamic['features'][i]['w'] = 1e7
if self._use_rolling_window:
try:
assert self._window_size > 0
except AssertionError:
self._dlm_logger.exception(
"Assertion exception occurred, zero window_size. "
"No rolling window will be used")
self._use_rolling_window = False
def __copy__(self):
"""Copies the object"""
result = super(DLMForecaster, self).__copy__()
#
result._dlm_trend = self._dlm_trend
result._dlm_seasonality = self._dlm_seasonality
result._dlm_dynamic = self._dlm_dynamic
result._dlm_auto_reg = self._dlm_auto_reg
result._dlm_long_season = self._dlm_long_season
result._use_rolling_window = self._use_rolling_window
result._window_size = self._window_size
result._dlm_interval_width = self._dlm_interval_width
result._dlm_logger = self._dlm_logger
return result
def set_params(self, p_dict=None, **kwargs):
"""Sets new parameters"""
params_dict = kwargs
if p_dict is not None:
params_dict = p_dict
#
for k, v in params_dict.items():
if k == 'ts_df':
self.ts_df = v
elif k == 'freq':
self.freq = v
elif k == 'n_test':
self.n_test = v
elif k == 'n_val':
self.n_val = v
elif k == 'timeformat':
self.time_format = v
elif k == "dlm_trend":
self._dlm_trend = v
elif k == "dlm_seasonality":
self._dlm_seasonality = v
elif k == "dlm_dynamic":
self._dlm_dynamic = v
elif k == "dlm_autoReg":
self._dlm_auto_reg = v
elif k == "dlm_longSeason":
self._dlm_long_season = v
# TBD other params!!
self.assertions()
return self
def get_params_dict(self):
"""Gets parameters as dictionary"""
return {
'dlm_trend': self._dlm_trend,
'dlm_seasonality': self._dlm_seasonality,
'dlm_dynamic': self._dlm_dynamic,
'dlm_auto_reg': self._dlm_auto_reg,
'dlm_long_season': self._dlm_long_season,
'use_rolling_window': self._use_rolling_window,
'window_size': self._window_size,
'dlm_interval_width': self._dlm_interval_width
}
def ts_split(self):
"""DLM extension of the parent ts_split()
DLM needs to extend the ts_split of its parent class.
The reason lies in dynamic features: this list of lists must be splitted
"""
# call super
super(DLMForecaster, self).ts_split()
if self._dlm_dynamic is None or self._mode == 'forecast':
return self
# split dynamic features
test_feat_dict = dict()
val_feat_dict = dict()
self._train_dlm_dynamic = self._dlm_dynamic
for i in range(len(self._dlm_dynamic)):
feats = self._dlm_dynamic['features'][i]['features']
#
if self._mode == 'test and validate':
if self._test_dlm_dynamic is not None:
self._train_dlm_dynamic['features'][i]['features'].append(
self._test_dlm_dynamic[self._dlm_dynamic['features'][i]
['name']])
self._val_dlm_dynamic = self._test_dlm_dynamic
else:
self._dlm_logger.error("Something is wrong, mode!")
else:
if self._mode == 'test' and self.n_val == 0:
self._train_dlm_dynamic['features'][i][
'features'] = feats[:(len(feats) - 1 - self.n_test)]
#
test_feat_dict[self._dlm_dynamic['features'][i]
['name']] = feats[(len(feats) -
self.n_test):]
elif self._mode == 'validate':
self._train_dlm_dynamic['features'][i][
'features'] = feats[:(len(feats) - 1 - self.n_val)]
#
val_feat_dict[self._dlm_dynamic['features'][i]
['name']] = feats[(len(feats) - self.n_val):]
elif self._mode == 'test' and self.n_val > 0:
self._dlm_dynamic['features'][i]['features'] = feats[:(
len(feats) - 1 - self.n_test - self.n_val)]
#
test_feat_dict[self._dlm_dynamic['features'][i]['name']] = \
feats[(len(feats) - self.n_test - self.n_val):(len(feats) - self.n_val - 1)]
val_feat_dict[self._dlm_dynamic['features'][i]
['name']] = feats[(len(feats) - self.n_val):]
# now set
if len(test_feat_dict):
self._test_dlm_dynamic = test_feat_dict
if len(val_feat_dict):
self._val_dlm_dynamic = val_feat_dict
return self
def ts_fit(self, suppress=False):
"""Fit DLM to the time series data.
Parameters:
----------
suppress: bool
Suppress or not some of the output messages
"""
self._prepare_fit()
self._model = None
self.ts_split()
ts_df = self._train_dt.copy()
# Fit
self._dlm_logger.info("Trying to fit the DLM model....")
try:
if not suppress:
self._dlm_logger.info("...via using parameters\n")
print_attributes(self)
ts_df = ts_df.reset_index()
ts_df.columns = self._ts_df_cols
self._model = dlm(ts_df['y'])
# trend
if self._dlm_trend is not None:
self._model = self._model + trend(
degree=self._dlm_trend['degree'],
discount=self._dlm_trend['discount'],
name=self._dlm_trend['name'],
w=self._dlm_trend['w'])
# seasonality
if self._dlm_seasonality is not None:
self._model = self._model + seasonality(
period=self._dlm_seasonality['period'],
discount=self._dlm_seasonality['discount'],
name=self._dlm_seasonality['name'],
w=self._dlm_seasonality['w'])
# dynamic
if self._train_dlm_dynamic is not None:
for i in range(len(self._train_dlm_dynamic['features'])):
self._model = self._model + dynamic(
features=self._train_dlm_dynamic['features'][i]
['features'],
discount=self._train_dlm_dynamic['features'][i]
['discount'],
name=self._train_dlm_dynamic['features'][i]['name'],
w=self._train_dlm_dynamic['features'][i]['w'])
# auto_reg
if self._dlm_auto_reg is not None:
self._model = self._model + autoReg(
degree=self._dlm_auto_reg['degree'],
discount=self._dlm_auto_reg['discount'],
name=self._dlm_auto_reg['name'],
w=self._dlm_auto_reg['w'])
# long_season
if self._dlm_long_season is not None:
ls = longSeason(period=self._dlm_long_season['period'],
stay=self._dlm_long_season['stay'],
data=ts_df,
name=self._dlm_long_season['name'],
w=self._dlm_long_season['w'])
self._model = self._model + ls
if not suppress:
self._dlm_logger.info("The constructed DLM model components:")
print(self._model.ls())
# tic
start = time()
if self._use_rolling_window:
self._model.fitForwardFilter(useRollingWindow=True,
windowLength=self._window_size)
self._model.fitBackwardSmoother()
else:
self._model.fit()
self.model_fit = self._model
# toc
if not suppress:
self._dlm_logger.info("Time elapsed: {} sec.".format(time() -
start))
except (Exception, ValueError) as e:
self._dlm_logger.exception("DLM error...{}".format(e))
return -1
else:
self._dlm_logger.info("Model successfully fitted to the data!")
self._dlm_logger.info("Computing fitted values and residuals...")
# Residuals
self.residuals = pd.Series(self.model_fit.getResidual(),
index=self._train_dt.index)
try:
self.lower_conf_int = pd.Series(
self.model_fit.getInterval()[1],
index=self._train_dt.index)
self.upper_conf_int = pd.Series(
self.model_fit.getInterval()[0],
index=self._train_dt.index)
except ValueError as e:
self._dlm_logger.exception(
"Something went wrong in getInterval...{}".format(e))
self.mse = self.model_fit.getMSE()
# Fitted values
# this is not elegant, but found no other way
self.fittedvalues = self._train_dt['y'] + self.residuals
return self
def ts_diagnose(self):
"""Diagnoses the fitted model"""
self.plot_residuals()
def plot_dlm(self):
"""Plot pydlm native plots"""
self.model_fit.plot("DLM native")
def plot_residuals(self):
"""Plot the residuals."""
fig, axis = super(DLMForecaster, self)._plot_residuals(
y=np.asarray(self._train_dt['y']),
yhat=np.asarray(self.fittedvalues),
_id="DLM")
plt.gcf().autofmt_xdate()
plt.grid(True)
plt.show()
def ts_test(self, show_plot=True):
"""Test the fitted model if test data available"""
if super(DLMForecaster, self)._check_ts_test() < 0:
return
N = len(self._test_dt)
self._dlm_logger.info(
"Evaluating the fitted DLM model on the test data...")
if self._test_dlm_dynamic is not None:
(predictMean, predictVar) = self._model.predictN(
N=N,
date=self._model.n - 1,
featureDict=self._test_dlm_dynamic)
else:
(predictMean,
predictVar) = self._model.predictN(N=N, date=self._model.n - 1)
self.forecast = pd.Series(np.asarray(predictMean),
index=self._test_dt.index)
# confidence intervals
cl, cu = self.compute_ci(yhat=np.asarray(predictMean),
yhat_var=np.asarray(predictVar),
ci_level=self._dlm_interval_width)
cl = pd.Series(cl, index=self._test_dt.index)
cu = pd.Series(cu, index=self._test_dt.index)
self.lower_conf_int = pd.concat([self.lower_conf_int, cl], axis=0)
self.upper_conf_int = pd.concat([self.upper_conf_int, cu], axis=0)
self.residuals_forecast = pd.Series(np.asarray(self._test_dt['y']) -
np.asarray(self.forecast),
index=self._test_dt.index)
self.measure_rmse()
self._dlm_logger.info("RMSE on test data: {}".format(self.rmse))
# plot
if show_plot:
self.plot_forecast()
return self
def ts_forecast(self, n_forecast, suppress=False, features_dict=None):
"""Forecast time series over time frame in the future specified via n_forecast"""
#
n_forecast = super(DLMForecaster, self)._check_ts_forecast(n_forecast)
#
self._dlm_logger.info("Fitting using all data....")
self._mode = 'forecast'
self.ts_fit(suppress=suppress)
self._dlm_logger.info("Forecasting next " + str(n_forecast) +
str(self.freq))
#
try:
if features_dict is not None and len(features_dict) != 0:
(predictMean,
predictVar) = self._model.predictN(N=n_forecast,
date=self._model.n - 1,
featureDict=features_dict)
else:
(predictMean,
predictVar) = self._model.predictN(N=n_forecast,
date=self._model.n - 1)
except (NameError, ValueError) as e:
self._dlm_logger.exception("DLM PredictN error...{}".format(e))
sys.exit("STOP")
idx_future = self._gen_idx_future(n_forecast=n_forecast)
self.forecast = pd.Series(np.asarray(predictMean), index=idx_future)
# confidence intervals
cl, cu = self.compute_ci(yhat=np.asarray(predictMean),
yhat_var=np.asarray(predictVar),
ci_level=self._dlm_interval_width)
cl = pd.Series(cl, index=idx_future)
cu = pd.Series(cu, index=idx_future)
self.lower_conf_int = pd.concat([self.lower_conf_int, cl], axis=0)
self.upper_conf_int = pd.concat([self.upper_conf_int, cu], axis=0)
self.residuals_forecast = None
self.plot_forecast(n_forecast=n_forecast, features_dict=features_dict)
return self
def plot_forecast(self, **kwargs):
"""Plot forecasted values"""
if self.residuals_forecast is not None:
fig, axis = super(DLMForecaster, self)._plot_forecast(
y=np.asarray(self._train_dt['y']),
yhat=np.asarray(self.fittedvalues),
forecast=self.forecast,
_id='DLM')
plt.gcf().autofmt_xdate()
plt.grid(True)
plt.show()
else:
n_forecast = -1
features_dict = dict()
for k, v in kwargs.items():
if k == 'n_forecast':
n_forecast = v
if k == 'features_dict':
features_dict = v
print(features_dict)
try:
if features_dict is not None and len(features_dict) != 0:
self._model.plotPredictN(N=n_forecast,
date=self._model.n - 1,
featureDict=features_dict)
else:
self._model.plotPredictN(N=n_forecast,
date=self._model.n - 1)
except (NameError, ValueError) as e:
self._dlm_logger.exception(
"DLM plotPredictN error...{}".format(e))
sys.exit("STOP")
class ARIMAForecaster(UVariateTimeSeriesClass):
"""Univariate time series child class for forecasting using ARIMA
Attributes
----------
_order: tuple
a tuple of p, d, q
_arima_trend: str
A parameter for controlling a model of the deterministic trend as one of ‘nc’ or ’c’.
‘c’ includes constant trend, ‘nc’ no constant for trend.
_arima_logger: Logger
the logger
Methods
----------
assertions()
Assertion tests, must be overrided
set_params()
Sets new parameter values
get_params_dict()
Gets parameter values as a dictionary
ts_fit()
Fits the ARIMA model to time series
ts_diagnose()
Diagnoses the fitted model
plot_residuals()
Generates residual plots
ts_test()
Evaluates fitted model on the test data, if this one has been generated
ts_forecast()
Forecasts time series and plots the results
plot_forecasts()
Plots forecasted time-series
"""
def __init__(self,
order=(1, 0, 1),
**kwds):
"""Initializes the object ARIMAForecaster"""
self._arima_logger = Logger("ARIMA")
self._order = order
self._arima_trend = ''
try:
super(ARIMAForecaster, self).__init__(**kwds)
except TypeError as e:
self._arima_logger.exception("Arguments missing...{}".format(e))
self._model = None
ARIMAForecaster._init_trend(self)
self._ar_coef = None
self._ma_coef = None
ARIMAForecaster.assertions(self)
self._id = 'ARIMA'
def _init_trend(self):
if self._trend == 'constant':
self._arima_trend = 'c'
elif self._trend is None:
self._arima_trend = 'nc'
elif self._trend in ['linear', 'constant linear', 'additive', 'add', 'multiplicative', 'mul']:
# self._arima_logger.warning("The trend " + str(self._trend) +
# " is not supported by ARIMA! Assuming constant trend")
self._arima_trend = 'c'
def __copy__(self):
"""Copies the object"""
result = super(ARIMAForecaster, self).__copy__()
result._order = self._order
result._arima_trend = self._arima_trend
result._arima_logger = self._arima_logger
return result
def assertions(self):
try:
assert isinstance(self._order, tuple)
except AssertionError:
self._arima_logger.exception("Assertion exception occurred, tuple expected")
sys.exit("STOP")
try:
assert (self.hyper_params is not None and len(self.hyper_params) != 0 and
'trend' in list(self.hyper_params.keys())) or (
self._arima_trend in ['c', 'nc'] or self._arima_trend is None)
except AssertionError:
self._arima_logger.exception("Assertion Error, trend must be in ['c', 'nc']")
sys.exit("STOP")
def set_params(self, p_dict=None, **kwargs):
"""Sets new parameter values"""
params_dict = kwargs
if p_dict is not None:
params_dict = p_dict
#
for k, v in params_dict.items():
if k == 'ts_df':
self.ts_df = v
elif k == 'freq':
self.freq = v
elif k == 'n_test':
self.n_test = v
elif k == 'n_val':
self.n_val = v
elif k == 'time_format':
self.time_format = v
elif k == 'order':
self._order = v
elif k == 'trend':
self._arima_trend = v
self.assertions()
return self
def get_params_dict(self):
"""Gets parameter values"""
return {'order': self._order,
'trend': self._arima_trend
}
def ts_fit(self, suppress=False):
"""Fit ARIMA to the time series data.
Parameters:
----------
suppress: bool
Suppress or not some of the output messages
"""
if self.hyper_params is not None:
self._gs.set_forecaster(self)
self._gs.set_hyper_params(self.hyper_params)
# a very important command here to avoid endless loop
self.hyper_params = None
self._arima_logger.info("***** Starting grid search *****")
self._gs = self._gs.grid_search(suppress=suppress, show_plot=False)
#
self.best_model = self._gs.best_model
self.__dict__.update(self.best_model['forecaster'].__dict__)
self._arima_logger.info("***** Finished grid search *****")
else:
self._prepare_fit()
self.ts_split()
ARIMAForecaster._init_trend(self)
ts_df = self._train_dt.copy()
# Fit
self._arima_logger.info("Trying to fit the ARIMA model....")
# tic
start = time()
try:
if not suppress:
self._arima_logger.info("...via using parameters\n")
print_attributes(self)
self._model = ARIMA(ts_df['y'], order=self._order, freq=self.freq)
self.model_fit = self._model.fit(trend=self._arima_trend, method='mle', disp=1)
except (Exception, ValueError):
self._arima_logger.exception("Exception occurred in the fit...")
self._arima_logger.error("Please try other parameters!")
self.model_fit = None
else:
# toc
self._arima_logger.info("Time elapsed: {} sec.".format(time() - start))
self._arima_logger.info("Model successfully fitted to the data!")
if not suppress:
self._arima_logger.info("The model summary: " + str(self.model_fit.summary()))
# Fitted values
self._arima_logger.info("Computing fitted values and residuals...")
self._ar_coef, self._ma_coef = self.model_fit.arparams, self.model_fit.maparams
self.fittedvalues = self.model_fit.fittedvalues
# prologue
if len(self.fittedvalues) != len(self._train_dt):
self.fittedvalues = pd.DataFrame(
index=pd.date_range(ts_df.index[0], ts_df.index[len(ts_df) - 1],
freq=self.freq),
columns=['dummy']).join(pd.DataFrame(self.fittedvalues)).drop(['dummy'], axis=1)
self.fittedvalues = self.fittedvalues.reset_index()
self.fittedvalues.columns = self._ts_df_cols
self.fittedvalues.set_index('ds', inplace=True)
self.fittedvalues.y = self.fittedvalues.y.fillna(method='bfill')
# Residuals
super(ARIMAForecaster, self)._residuals()
self._arima_logger.info("Done.")
return self
def ts_diagnose(self):
"""Diagnoses the model.
In case of ARIMA residual plots are generated.
Additionally, the kde plot of residuals is returned
"""
try:
assert self.model_fit is not None
except AssertionError:
self._arima_logger.exception("Model has to be fitted first! Please call ts_fit(...)")
sys.exit("STOP")
self.residuals.plot(kind='kde', title='Density')
print("Residuals statistics")
print(self.residuals.describe())
self.plot_residuals()
def plot_residuals(self):
"""Plot the residuals"""
fig, axis = super(ARIMAForecaster, self)._plot_residuals(y=np.asarray(self._train_dt['y']),
yhat=np.asarray(self.fittedvalues).flatten(),
_id="ARIMA")
plt.gcf().autofmt_xdate()
plt.grid(True)
plt.show()
def ts_test(self, show_plot=True):
"""Test the fitted model if test data available"""
if super(ARIMAForecaster, self)._check_ts_test() < 0:
return
n_forecast = len(self._test_dt)
self._arima_logger.info("Evaluating the fitted ARIMA model on the test data...")
future = self.model_fit.predict(start=len(self._train_dt.index),
end=len(self._train_dt.index) + n_forecast - 1, dynamic=True)
self.forecast = pd.Series(future, index=self._test_dt.index)
self.residuals_forecast = pd.Series(np.asarray(self._test_dt['y']) - np.asarray(self.forecast),
index=self._test_dt.index)
self.measure_rmse()
self._arima_logger.info("RMSE on the test data: {}".format(self.rmse))
# plot
if show_plot:
self.plot_forecast()
def ts_forecast(self, n_forecast, suppress=False):
"""Forecast time series over time frame in the future specified via n_forecast"""
#
n_forecast = super(ARIMAForecaster, self)._check_ts_forecast(n_forecast)
#
self._arima_logger.info("Fitting using all data....")
self._mode = 'forecast'
self.ts_fit(suppress=suppress)
self._arima_logger.info("Forecasting next " + str(n_forecast) + str(self.freq))
#
future = self.model_fit.predict(start=len(self._train_dt.index),
end=len(self._train_dt.index) + (n_forecast-1), dynamic=True)
idx_future = self._gen_idx_future(n_forecast=n_forecast)
self.forecast = pd.Series(future, index=idx_future)
self.residuals_forecast = None
# self.plot_forecast()
return self
def plot_forecast(self):
"""Plot forecasted values"""
fig, axis = super(ARIMAForecaster, self)._plot_forecast(y=np.asarray(self._train_dt['y']),
yhat=np.asarray(self.fittedvalues).flatten(),
forecast=self.forecast, _id='ARIMA')
plt.gcf().autofmt_xdate()
plt.grid(True)
plt.show()
class UVariateTimeSeriesClass(object):
"""Univariate time series class
Attributes
----------
_ts_df_cols: list
internal column names for dataframe that will be input to model
ts_df: dataframe
time series data frame
freq: int
frequency of time series; python format
fill_method: str
filling method for resampled data. Possible are 'ffill' and 'interp1d'
n_test: int
number of units (defined by frequency, e.g. 6 days) to use as test data. 0 would mean no test data is generated.
n_val: int
similar to n_test, for validation
time_format: str
time format if time series data needs to be brought into datetime
_mode: str
defines the mode as 'test' or 'forecast'
_train_dt: dataframe
training data
_test_dt: dataframe
test data
_val_dt: dataframe
validation data
model_fit:
fitted model
fittedvalues: series
computed fitted values
residuals: series
residuals
rmse: float
RMSE on test set (test data and the forecast on test data)
_gs: GridSearchClass
The grid search class for model optimization in case hyper_parameters are specified
hyper_params: dictionary
The dictionary of hyper parameters or None if no model optimization wished
best_model: dictionary
The best model resulted from the grid search
upper_whisker_res: float
upper whisker for residuals
lower_conf_int: series
lower confidence interval
upper_conf_int: series
upper confidence interval
_trend: str or iterable, default=’c’
in AutoARIMA:
ref. http://www.alkaline-ml.com/pmdarima/1.0.0/modules/generated/pmdarima.arima.auto_arima.html
Parameter controlling the deterministic trend polynomial A(t). Can be specified as a string where ‘c’
indicates a constant (i.e. a degree zero component of the trend polynomial),
‘t’ indicates a linear trend with time, and ‘ct’ is both.
Can also be specified as an iterable defining the polynomial as
in numpy.poly1d, where [1,1,0,1] would denote a+bt+ct3.
in ARIMA:
A parameter for controlling a model of the deterministic trend as one of ‘nc’ or ’c’.
‘c’ includes constant trend, ‘nc’ no constant for trend.
in SARIMA:
A parameter for controlling a model of the deterministic trend as one of ‘n’,’c’,’t’,’ct’ for no trend,
constant, linear, and constant with linear trend, respectively.
in ExponentialSmoothing:
The type of trend component, as either “add” for additive or “mul” for multiplicative.
Modeling the trend can be disabled by setting it to None
_test: list or str
in ARIMA:
list of possible tests for determining d
in AutoARIMA.
test for determining the value of d, e.g. 'adf'
_seasonal: bool or str
in AutoARIMA
Seasonal component yes/no
in ExponentialSmoothing:
The type of seasonal component, as either “add” for additive or “mul” for multiplicative.
Modeling the seasonal component can be disabled by setting it to None
_seasonal_periods: int
The number of time steps in a seasonal period, e.g. 12 for 12 months in a yearly seasonal structure
forecast: series
computed forcatsed values
residuals_forecast: series
residuals between forecasted and real values. Note, this variable exist only if test data existed
Methods
-------
ts_transform()
Transforms time series using log10 or box-cox
ts_resample()
Resamples time series at the chosen frequency freq
ts_test()
Evaluates fitted model on the test data, if this one has been generated
ts_forecast()
Forecasts time series and plots the results
ts_decompose()
Decomposes time series in _arr_seasonal, _arr_trend, residual(irregular) and _arr_baseline,
and plots the results
plot_decompose()
Plots the results of ts_decompose()
difference()
Differences the time series given the lag (parameter interval)
rolling_mean()
Computes moving average given the window size
rolling_variance()
Computes moving variance given the window size
test_adf():
ADF test for stationarity
test_kpss():
KPSS test for stationarity
ndiff()
Determines value for diff parameter d
All tests given in the parameter tests are applied
acf_plots()
Generates autocorrelation plots
pacf_plots()
Generates partial correlation plots
Helper methods:
-------
_plot_residuals()
Residual plots helper function
_plot_forecast()
Helper function for plotting forecasted time-series
_prepare_fit()
Prepares ts_fit of child class. Supposed to be called by a child class
_residuals()
Helper function for calculating residuals. Supposed to be called by a child class
_check_ts_test()
Checks for test. Supposed to be called by a child class
_check_ts_forecast()
Checks for forecast. Supposed to be called by a child class
"""
def __init__(self,
ts_df,
time_format="%Y-%m-%d %H:%M:%S",
freq='D',
fill_method='ffill',
n_test=0,
n_val=0,
hyper_params=None,
test='adf',
trend=None,
seasonal=False,
seasonal_periods=1,
**kwds):
"""Initializes the object UVariateTimeSeriesForecaster"""
self._ts_df_cols = ['ds', 'y']
self.ts_df = ts_df
self.time_format = time_format
self.freq = freq
self.fill_method = fill_method.lower()
self.n_test = int(n_test)
self.n_val = int(n_val)
self.transform = None
self._boxcox_lmbda = None
self._mode = ''
self._train_dt = None
self._test_dt = None
self._val_dt = None
self.model_fit = None
self.fittedvalues = None
self.residuals = None
self.rmse = 0
self._gs = tsa.GridSearchClass()
self.hyper_params = hyper_params
self.best_model = dict()
"""
self.rmse_test = 0
self.rmse_val = 0
"""
self.upper_whisker_res = None
self.lower_conf_int = None
self.upper_conf_int = None
self.forecast = None
self.residuals_forecast = None
self._res_decomp = None
self._arr_seasonal = None
self._arr_trend = None
self._arr_baseline = None
self._test = test
self._trend = trend
if self._trend is not None:
self._trend = self._trend.lower()
self._seasonal = seasonal
if isinstance(self._seasonal, str):
self._seasonal = self._seasonal.lower()
self._seasonal_periods = seasonal_periods
self._uvts_cls_logger = Logger('uvts_cls')
UVariateTimeSeriesClass.assertions(self)
# work with ts_df
self.ts_df = self.ts_df.reset_index()
self.ts_df.columns = self._ts_df_cols
self.ts_df['y'] = self.ts_df['y'].apply(np.float64, errors='coerce')
self.ts_df.set_index('ds', inplace=True)
self._uvts_cls_logger.info("Received time series data of range: " +
str(min(self.ts_df.index)) + ' - ' +
str(max(self.ts_df.index)) +
" and shape: " + str(self.ts_df.shape))
if not isinstance(self.ts_df.index, pd.DatetimeIndex):
self._uvts_cls_logger.warning("Time conversion required...")
self.ts_df = self.ts_df.reset_index()
try:
self.ts_df['ds'] = self.ts_df['ds'].apply(
lambda x: datetime.datetime.strptime(
str(x).translate({
ord('T'): ' ',
ord('Z'): None
})[:-1], self.time_format))
except ValueError as e:
self._uvts_cls_logger.warning(
"Zulu time conversion not successful: {}".format(e))
self._uvts_cls_logger.warning(
"Will try without assuming zulu time...")
try:
self.ts_df['ds'] = self.ts_df['ds'].apply(
lambda x: datetime.datetime.strptime(
str(x), self.time_format))
except ValueError as e:
self._uvts_cls_logger.info(
"Time conversion not successful. Check your time_format: {}"
.format(e))
sys.exit("STOP")
else:
self._uvts_cls_logger.info("Time conversion successful!")
else:
self._uvts_cls_logger.info("Time conversion successful!")
# set index
self.ts_df.set_index('ds', inplace=True)
#
self.ts_df.index = pd.to_datetime(self.ts_df.index)
self.ts_df.sort_index(inplace=True)
# resample
self.ts_resample()
UVariateTimeSeriesClass.assertions(self, post=True)
#
if self.n_val > len(self.ts_df) - self.n_test:
self.n_val = len(self.ts_df) - self.n_test
if self.n_test == 0 and self.n_val == 0:
self._mode = 'forecast'
elif self.n_test > 0:
self._mode = 'test'
elif self.n_test == 0 and self.n_val > 0:
self._mode = 'validate'
# delegate just for good programming style here
super(UVariateTimeSeriesClass, self).__init__(**kwds)
def assertions(self, post=False):
if post:
try:
assert 0 <= self.n_test < len(self.ts_df)
except AssertionError:
self._uvts_cls_logger.exception(
"Assertion exception, invalid value for n_test!")
sys.exit("STOP")
#
try:
assert 0 <= self.n_val < len(self.ts_df)
except AssertionError:
self._uvts_cls_logger.exception(
"Assertion exception, invalid value for n_val!")
sys.exit("STOP")
else:
try:
assert self.fill_method in ['ffill', 'interp1d']
except AssertionError:
self._uvts_cls_logger.exception(
"Assertion exception, fill method not recognized! "
"'ffill' will be used. ")
else:
self.fill_method = 'ffill'
try:
assert pd.DataFrame(self.ts_df).shape[1] <= 2
except AssertionError:
self._uvts_cls_logger.exception(
"Time series must be uni-variate. "
"Hence, at most a time columns and a column of numeric values are expected!"
)
sys.exit("STOP")
try:
self._trend is None or (isinstance(
self._trend, str) and self._trend in [
'constant', 'linear', 'constant linear', 'additive',
'add', 'multiplicative', 'mul'
])
except AssertionError:
self._uvts_cls_logger.exception(
"Assertion exception occurred, invalid value for trend! "
"Choose between None or "
"['constant', 'linear ','constant linear', "
"'additive', 'add , 'multiplicative', 'mul'] ")
sys.exit("STOP")
try:
self._seasonal is None or isinstance(self._seasonal, bool) or (
isinstance(self._seasonal, str) and self._seasonal
in ['additive', 'add', 'multiplicative', 'mul'])
except AssertionError:
self._uvts_cls_logger.exception(
"Assertion exception occurred, invalid value for seasonal! "
"Choose between True/False, None or "
"['additive', 'add , 'multiplicative', 'mul'] ")
sys.exit("STOP")
def __copy__(self):
"""Copies the object"""
cls = self.__class__
result = cls.__new__(cls)
result.__dict__.update(self.__dict__)
return result
def ts_transform(self, transform):
"""Transforms time series via applying casted 'transform'. Right now 'log10' and 'box-cox' possible."""
try:
assert transform.lower().strip() in ['log10', 'box-cox']
except AssertionError:
self._uvts_cls_logger.error(
"transform should be in ['log10', 'box-cox'] or empty. Assuming no transform! "
"Hence, if you get bad results, you would like maybe to choose e.g., log10 here."
)
self._uvts_cls_logger.exception(
"Assertion exception occurred, transform")
self.transform = None
else:
self.transform = transform.lower()
# transform
if sum(self.ts_df['y'] > 0) < len(self.ts_df['y']):
self._uvts_cls_logger.warning(
"Zero, negative, or both values present in your data. Transformation will not be used!"
)
return self
if self.transform == 'log10':
try:
self.ts_df['y'] = self.ts_df['y'].apply(np.log10)
except ValueError:
self._uvts_cls_logger.exception(
"log10 transformation did not work! Possibly negative "
"values present?")
elif self.transform == 'box-cox':
if input("Do you want to provide lambda for box.cox? y/n?"
).strip().lower() == 'y':
self._boxcox_lmbda = float(input())
else:
self._boxcox_lmbda = None
try:
if self._boxcox_lmbda is None:
bc, lmbda_1 = stats.boxcox(self.ts_df['y'],
lmbda=self._boxcox_lmbda)
self.ts_df['y'] = stats.boxcox(self.ts_df['y'],
lmbda=lmbda_1)
else:
self.ts_df['y'] = stats.boxcox(
self.ts_df['y'], lmbda=self._boxcox_lmbda)
except ValueError:
self._uvts_cls_logger.exception(
"box-cox transformation did not work! "
"Possibly negative values present or bad lambda?")
return self
def set_frequency(self, new_freq):
"""Sets new frequency and resamples time series to that new frequency"""
self.freq = new_freq
self.ts_resample()
def ts_check_frequency(self):
"""Checks the frequency of time series"""
if self.ts_df.index.freq is None:
self._uvts_cls_logger.info("No specific frequency detected.")
self._uvts_cls_logger.info(
"Frequency chosen in initialization: " + str(self.freq) +
" enter 'n' and call ts_resample() if you are satisfied with this value."
)
if input("Should a histogram of time deltas be plotted y/n?"
).strip().lower() == 'y':
ff = pd.Series(self.ts_df.index[1:(len(self.ts_df))] -
self.ts_df.index[0:(len(self.ts_df) - 1)])
ff = ff.apply(lambda x: int(x.total_seconds() / (60 * 60)))
plt.hist(ff, bins=120)
plt.xlabel("Rounded time delta [H]")
plt.ylabel("Frequency of occurrence")
self._uvts_cls_logger.info(ff.value_counts())
self._uvts_cls_logger.info(
"Should hourly frequency not fit, choose a reasonable frequency and call "
"set_frequency(new_freq)")
else:
pass
else:
self._uvts_cls_logger.info("Time series frequency: " +
str(self.ts_df.index.freq))
def ts_resample(self):
"""Brings original time series to the chosen frequency freq"""
try:
ts_freq = pd.DataFrame(index=pd.date_range(
self.ts_df.index[0],
self.ts_df.index[len(self.ts_df) - 1],
freq=self.freq),
columns=['dummy'])
except ValueError:
self._uvts_cls_logger.exception(
"Exception occurred, possibly incompatible frequency!")
sys.exit("STOP")
if self.fill_method == 'ffill':
self.ts_df = ts_freq.join(self.ts_df).drop(['dummy'], axis=1)
self.ts_df.y = self.ts_df.y.fillna(method='ffill')
# if np.isnan ( self.ts_df.y ).any ():
# self.ts_df.y = self.ts_df.y.fillna ( method='bfill' )
else: # interp
xp = np.linspace(0,
self.ts_df.size,
self.ts_df.size,
endpoint=False)
fp = self.ts_df['y']
# join
self.ts_df = ts_freq.join(self.ts_df).drop(['dummy'], axis=1)
# pick new points
x = np.linspace(0, ts_freq.size, ts_freq.size, endpoint=False)
x = x[self.ts_df['y'].isna()]
print(x.size)
print(x)
# put the values
self.ts_df.y[self.ts_df['y'].isna()] = np.interp(x, xp, fp)
if np.isnan(self.ts_df.y).any():
self._uvts_cls_logger.warning(
"Some NaN found, something went wrong, check the data!")
sys.exit("STOP")
self._uvts_cls_logger.info("Time series resampled at frequency: " +
str(self.ts_df.index.freq) +
". New shape of the data: " +
str(self.ts_df.shape))
self._uvts_cls_logger.info("Using time series data of range: " +
str(min(self.ts_df.index)) + ' - ' +
str(max(self.ts_df.index)) +
" and shape: " + str(self.ts_df.shape))
return self
def ts_split(self):
"""Prepares data for different modes: train, test, validate, test and validate, forecast"""
if self.ts_df.index.freq is None:
self._uvts_cls_logger.warning(
"Time series exhibit no frequency. Calling ts_resample()...")
try:
self.ts_resample()
except ValueError:
self._uvts_cls_logger.error("Resample did not work! Error:" +
str(sys.exc_info()[0]))
ts_df = self.ts_df
if self._mode == 'forecast':
self._train_dt = ts_df
self._test_dt, self._val_dt = None, None
elif self._mode == 'test and validate':
if self._test_dt is not None:
self._train_dt = pd.concat([self._train_dt, self._test_dt],
axis=0)
self._test_dt = self._val_dt
self._val_dt = None
else:
self._uvts_cls_logger.error("Something is wrong: mode!")
else:
# split
ts_test_df = pd.DataFrame()
ts_val_df = pd.DataFrame()
#
ts_df = ts_df.reset_index()
ts_df.columns = self._ts_df_cols
if self._mode == 'test' and self.n_val == 0:
ts_test_df = ts_df.copy()
#
ts_df = pd.DataFrame(ts_df.loc[:(len(ts_df) - 1 -
self.n_test), ])
ts_df.set_index('ds', inplace=True)
# test
ts_test_df = pd.DataFrame(ts_test_df.loc[(len(ts_test_df) -
self.n_test):, ])
ts_test_df.set_index('ds', inplace=True)
elif self._mode == 'validate':
ts_val_df = ts_df.copy()
#
ts_df = pd.DataFrame(ts_df.loc[:(len(ts_df) - 1 -
self.n_val), ])
ts_df.set_index('ds', inplace=True)
# val
ts_val_df = pd.DataFrame(ts_val_df.loc[(len(ts_val_df) -
self.n_val):, ])
ts_val_df.set_index('ds', inplace=True)
elif self._mode == 'test' and self.n_val > 0:
ts_test_df = ts_df.copy()
ts_val_df = ts_df.copy()
#
ts_df = pd.DataFrame(ts_df.loc[:(len(ts_df) - 1 - self.n_test -
self.n_val), ])
ts_df.set_index('ds', inplace=True)
# test
ts_test_df = pd.DataFrame(
ts_test_df.loc[(len(ts_test_df) - self.n_test -
self.n_val):(len(ts_test_df) - self.n_val -
1)])
ts_test_df.set_index('ds', inplace=True)
# val
ts_val_df = pd.DataFrame(ts_val_df.loc[(len(ts_val_df) -
self.n_val):, ])
ts_val_df.set_index('ds', inplace=True)
# now set
self._train_dt = ts_df
if not ts_test_df.empty:
self._test_dt = ts_test_df
if not ts_val_df.empty:
self._val_dt = ts_val_df
return self
@staticmethod
def compute_ci(yhat, yhat_var, ci_level):
"""Easy compute of confidence intervals"""
z_mapping = {0.95: 1.96, 0.99: 2.58}
z = z_mapping[ci_level]
ci_lower = yhat - yhat_var * z
ci_upper = yhat + yhat_var * z
return ci_lower, ci_upper
def _prepare_fit(self):
"""Helper function ro prepare ts_fit"""
self.lower_conf_int, self.upper_conf_int, self.upper_whisker_res = None, None, None
self.model_fit = None
self.residuals, self.residuals_forecast, self.fittedvalues = None, None, None
def _residuals(self):
"""Helper function to calculate residuals"""
if self.model_fit is None:
self._uvts_cls_logger.error(
"No model has been fitted, residuals cannot be computed!")
sys.exit("STOP")
try:
# use fittedvalues to fill in the model dictionary
self.residuals = pd.Series(np.asarray(self._train_dt['y']) -
np.asarray(self.fittedvalues).flatten(),
index=self._train_dt['y'].index)
self.upper_whisker_res = self.residuals.mean() + 1.5 * (
self.residuals.quantile(0.75) - self.residuals.quantile(0.25))
except (KeyError, AttributeError):
self._uvts_cls_logger.exception(
"Exception occurred: Model was not fitted or ts has other structure"
)
return self
def _plot_residuals(self, y, yhat, _id):
"""Helper function to plot the residuals"""
try:
assert self.model_fit is not None
except AssertionError:
self._uvts_cls_logger.exception(
"Model has to be fitted first! Please call ts_fit(...)")
fig, axes = plt.subplots(2, 1, figsize=(20, 5), sharex=True)
axes[0].plot(pd.Series(yhat, index=self._train_dt.index),
color='y',
linewidth=2.0)
axes[0].plot(pd.Series(y, index=self._train_dt.index), color='b')
axes[0].set_ylabel("Model Fit")
axes[0].set_title("Real (blue) and estimated values, " + str(_id))
#
axes[1].plot(self.residuals, color="r")
"""
if self.forecast is not None and self.residuals_forecast is None \
and self.lower_conf_int is not None and self.upper_conf_int is not None:
axes[0].fill_between(self.lower_conf_int.index, self.lower_conf_int, self.upper_conf_int, color='k',
alpha=.15)
"""
if self.lower_conf_int is not None and self.upper_conf_int is not None:
axes[0].fill_between(self.lower_conf_int.index,
self.lower_conf_int,
self.upper_conf_int,
color='k',
alpha=.15)
if self.upper_whisker_res is not None:
axes[1].axhline(y=self.upper_whisker_res,
xmin=0,
xmax=1,
color='m',
label='upper_whisker',
linestyle='--',
linewidth=1.5)
axes[1].axhline(y=-self.upper_whisker_res,
xmin=0,
xmax=1,
color='m',
label='upper_whisker',
linestyle='--',
linewidth=1.5)
axes[1].set_ylabel('Residuals')
axes[1].set_title(
'Difference between model output and the real data and +/- upper whisker, '
+ str(_id))
return fig, axes
def _check_ts_test(self):
"""Check before ts_test in child class is called"""
try:
assert self.model_fit is not None
except AssertionError:
self._uvts_cls_logger.exception(
"Model has to be fitted first! Please call ts_fit(...)")
try:
assert self._test_dt is not None
except (KeyError, AssertionError):
self._uvts_cls_logger.exception(
"Nothing to test. "
"Call ts_forecast() or specify amount of test data "
"when initializing the object.")
return -1
else:
# self._mode = 'test'
return 0
def _check_ts_forecast(self, n_forecast):
"""Check before ts_forecast in child class is called"""
#
try:
n_forecast = int(n_forecast)
assert 0 < n_forecast < len(self._train_dt)
except AssertionError:
self._uvts_cls_logger.exception(
"Number of periods to be forecasted is too low, too high or not numeric!"
)
except ValueError:
self._uvts_cls_logger.exception(
"n_forecast must be convertible to int type!")
return n_forecast
def _gen_idx_future(self, n_forecast):
"""Generate the time axis for future data"""
idx_future = None
if self.freq == 'S':
idx_future = pd.date_range(start=max(self._train_dt.index) +
datetime.timedelta(seconds=1),
end=max(self._train_dt.index) +
datetime.timedelta(seconds=n_forecast),
freq='S')
elif self.freq == 'min':
idx_future = pd.date_range(start=max(self._train_dt.index) +
datetime.timedelta(minutes=1),
end=max(self._train_dt.index) +
datetime.timedelta(minutes=n_forecast),
freq='min')
elif self.freq == 'H':
idx_future = pd.date_range(start=max(self._train_dt.index) +
datetime.timedelta(hours=1),
end=max(self._train_dt.index) +
datetime.timedelta(hours=n_forecast),
freq='H')
elif self.freq == 'D':
idx_future = pd.date_range(start=max(self._train_dt.index) +
datetime.timedelta(days=1),
end=max(self._train_dt.index) +
datetime.timedelta(days=n_forecast),
freq='D')
elif self.freq == 'W':
idx_future = pd.date_range(start=max(self._train_dt.index) +
datetime.timedelta(weeks=1),
end=max(self._train_dt.index) +
datetime.timedelta(weeks=n_forecast),
freq='W')
elif self.freq == 'M' or self.freq == 'MS':
idx_future = pd.date_range(start=max(self._train_dt.index) +
relativedelta(months=+1),
end=max(self._train_dt.index) +
relativedelta(months=+n_forecast),
freq=self.freq)
return idx_future
def _prepare_forecast(self, yhat, forecast):
# forecast
forecast = forecast.reset_index()
forecast.columns = self._ts_df_cols
forecast.set_index('ds', inplace=True)
#
vals = list()
vals.append(yhat[-1])
for i in range(len(forecast['y'])):
vals.append(forecast['y'][i])
idx = list()
idx.append(self._train_dt.index[-1])
for i in range(len(forecast.index)):
idx.append(forecast.index[i])
#
return pd.Series(vals, index=idx)
def _plot_forecast(self, y, yhat, forecast, _id):
"""Helper function to plot forecasted values"""
try:
assert self.model_fit is not None
except AssertionError:
self._uvts_cls_logger.exception(
"Model has to be fitted first! Please call ts_fit(...)")
sys.exit("STOP")
#
try:
assert self.forecast is not None
except AssertionError:
self._uvts_cls_logger.exception(
"Neither ts_test(...) nor ts_forecast(...) have been called yet!"
)
sys.exit("STOP")
fig, axes = plt.subplots(2, 1, figsize=(20, 7), sharex=True)
#
axes[0].plot(pd.Series(yhat, index=self._train_dt.index),
color='y',
linewidth=2.0)
axes[0].plot(pd.Series(y, index=self._train_dt.index),
color='b',
linewidth=1.0)
#
if self.residuals_forecast is not None:
axes[0].plot(self.ts_df, color='b')
forecast = self._prepare_forecast(yhat=yhat, forecast=forecast)
axes[0].plot(forecast, color='orange', linewidth=2.0)
#
if self.lower_conf_int is not None and self.upper_conf_int is not None:
axes[0].fill_between(self.lower_conf_int.index,
self.lower_conf_int,
self.upper_conf_int,
color='k',
alpha=.15)
axes[0].set_ylabel("Fit and Forecast/Validation")
axes[0].set_title(
"Real (blue), estimated (yellow) and forecasted values, " +
str(_id))
#
if self.residuals_forecast is not None:
axes[1].plot(pd.concat([self.residuals, self.residuals_forecast],
axis=0),
color='r')
axes[1].plot(self.residuals, color="r")
if self.upper_whisker_res is not None:
axes[1].axhline(y=self.upper_whisker_res,
xmin=0,
xmax=1,
color='m',
label='upper_whisker',
linestyle='--',
linewidth=1.5)
axes[1].axhline(y=-self.upper_whisker_res,
xmin=0,
xmax=1,
color='m',
label='upper_whisker',
linestyle='--',
linewidth=1.5)
axes[1].set_ylabel("Residuals")
axes[1].set_title(
"Difference between model output and the real data both, for fitted "
"and forecasted and +/- upper whisker or confidence intervals, " +
str(_id))
return fig, axes
def ts_decompose(self, params=None):
"""Decomposes time series"""
self._res_decomp = None
self._arr_seasonal = None
self._arr_trend = None
self._arr_baseline = None
self.residuals = None
if params is None:
params = dict({'model': 'additive', 'freq': 1})
try:
assert isinstance(params, dict)
except AssertionError:
self._uvts_cls_logger.exception(
"Dictionary is expected for parameters!")
sys.exit("STOP")
try:
assert 'model' in list(params.keys())
except AssertionError:
self._uvts_cls_logger.exception(
"Unexpected dictionary keys. At least decomposition "
"model must be supplied!")
sys.exit("STOP")
try:
assert params['model'].lower() in ['additive', 'multiplicative']
except AssertionError:
self._uvts_cls_logger.exception(
"Unexpected value for the parameter 'model'! "
"Choose from ['additive', 'multiplicative']")
sys.exit("STOP")
else:
params['model'] = params['model'].lower()
if 'freq' not in list(params.keys()):
params['freq'] = 1
try:
ts2decomp = self.ts_df
if 'from' in list(params.keys()):
ts2decomp = ts2decomp[
ts2decomp.index >= datetime.datetime.strptime(
params['from'], self.time_format)]
if 'to' in list(params.keys()):
ts2decomp = ts2decomp[ts2decomp.index <= datetime.datetime.
strptime(params['to'], self.time_format)]
try:
assert ts2decomp.size > 0
except AssertionError:
self._uvts_cls_logger.exception(
"Empty time series resulted, please check your parameters!"
)
sys.exit("STOP")
if ts2decomp.index.freq is not None:
res = seasonal_decompose(ts2decomp.loc[:, 'y'],
model=params['model'])
else:
res = seasonal_decompose(ts2decomp.loc[:, 'y'],
model=params['model'],
freq=params['freq'])
except ValueError:
self._uvts_cls_logger.exception(
"ValueError, seasonal_decompose error")
else:
self._res_decomp = res
self._arr_seasonal = res.seasonal
self._arr_trend = res.trend
self._arr_baseline = self._arr_seasonal + self._arr_trend
self.residuals = res.resid
self.upper_whisker_res = self.residuals.mean() + 1.5 * (
self.residuals.quantile(0.75) - self.residuals.quantile(0.25))
self.plot_decompose()
def ts_stl_decompose(self, params=None):
self._res_decomp = None
self._arr_seasonal = None
self._arr_trend = None
self._arr_baseline = None
self.residuals = None
if params is None:
params = dict({'period': 12})
try:
assert isinstance(params, dict)
except AssertionError:
self._uvts_cls_logger.exception(
"Dictionary is expected for parameters!")
sys.exit("STOP")
try:
assert 'period' in list(params.keys())
except AssertionError:
self._uvts_cls_logger.exception(
"Unexpected dictionary keys. At least decomposition "
"period must be supplied!")
sys.exit("STOP")
try:
assert isinstance(params['period'], int)
except AssertionError:
self._uvts_cls_logger.exception(
"Unexpected value for the parameter 'period'! "
"Integer expected")
sys.exit("STOP")
try:
ts2decomp = self.ts_df
if 'from' in list(params.keys()):
ts2decomp = ts2decomp[
ts2decomp.index >= datetime.datetime.strptime(
params['from'], self.time_format)]
if 'to' in list(params.keys()):
ts2decomp = ts2decomp[ts2decomp.index <= datetime.datetime.
strptime(params['to'], self.time_format)]
try:
assert ts2decomp.size > 0
except AssertionError:
self._uvts_cls_logger.exception(
"Empty time series resulted, please check your parameters!"
)
sys.exit("STOP")
res = decompose(ts2decomp, period=params['period'])
except ValueError:
self._uvts_cls_logger.exception("ValueError, stl_decompose error")
else:
self._res_decomp = res
self._arr_seasonal = res.seasonal
self._arr_trend = res.trend
self._arr_baseline = self._arr_seasonal + self._arr_trend
self.residuals = res.resid
self.upper_whisker_res = np.asarray(self.residuals.mean() + 1.5 * (
self.residuals.quantile(0.75) - self.residuals.quantile(0.25)))
self.plot_decompose()
def plot_decompose(self):
"""Plots the results of time series decomposition"""
try:
assert self._arr_seasonal is not None
except AssertionError:
self.ts_decompose()
fig, axes = plt.subplots(5, 1, figsize=(20, 9), sharex=True)
axes[0].plot(self._res_decomp.observed)
axes[0].set_ylabel("Original")
#
axes[1].plot(self._arr_trend)
axes[1].set_ylabel("Trend")
#
axes[2].plot(self._arr_seasonal)
axes[2].set_ylabel("Seasonal")
#
axes[3].plot(self._arr_baseline)
axes[3].set_ylabel("Baseline")
#
axes[4].plot(self.residuals)
axes[4].set_ylabel("Residuals")
#
if self.upper_whisker_res is not None:
axes[4].axhline(y=self.upper_whisker_res,
xmin=0,
xmax=1,
color='m',
label='upper_whisker',
linestyle='--',
linewidth=1.5)
axes[4].axhline(y=-self.upper_whisker_res,
xmin=0,
xmax=1,
color='m',
label='upper_whisker',
linestyle='--',
linewidth=1.5)
plt.gcf().autofmt_xdate()
plt.grid(True)
plt.show()
def difference(self, lag=1):
diff = list()
for i in range(lag, len(self.ts_df)):
value = self.ts_df['y'][i] - self.ts_df['y'][i - lag]
diff.append(value)
return pd.Series(diff)
def rolling_mean(self, window=10):
return self.ts_df.rolling(window=window).mean()
def rolling_variance(self, window=10):
return self.ts_df.rolling(window=window).std()
def test_adf(self):
"""Performs Dickey-Fuller test for stationarity"""
dftest = adfuller(self.ts_df['y'], autolag='AIC')
dfoutput = pd.Series(dftest[0:4],
index=[
'Test Statistic', 'p-value', '#Lags Used',
'Number of Observations Used'
])
for key, value in dftest[4].items():
dfoutput['Critical Value (%s)' % key] = value
print(dfoutput)
if dftest[0] > dftest[4]['5%']:
print(
"Test statistic greater than critical value at 5% --> series seems to be not stationary. "
"Look at critical values at 1% and 10% too, ideally they also should be less than test statistic."
)
else:
print(
"Test statistic less than critical value at 5% --> series seems to be stationary. "
"Look at critical values at 1% and 10% too, ideally they also should be greater than test statistic."
)
def test_kpss(self):
"""Performs Kwiatkowski-Phillips-Schmidt-Shin test for stationarity"""
kpsstest = kpss(self.ts_df['y'], regression='c')
kpss_output = pd.Series(
kpsstest[0:3], index=['Test Statistic', 'p-value', 'Lags Used'])
for key, value in kpsstest[3].items():
kpss_output['Critical Value (%s)' % key] = value
print(kpss_output)
if kpsstest[0] > kpsstest[3]['5%']:
print(
"Test statistic greater than critical value at 5% --> series seems to be not stationary. "
"Look at critical values at 1% and 10% too, ideally they also should be greater than test statistic."
)
else:
print(
"Test statistic less than critical value at 5% --> series seems to be stationary. "
"Look at critical values at 1% and 10% too, ideally they also should be less than test statistic."
)
def ndiff(self, tests=['kpss', 'adf', 'pp'], alpha=0.05, max_d=2):
"""Returns p-values to decide for the value of d-differentiation
list of tests given in tests parameter are applied.
"""
try:
assert sum([i in ['kpss', 'adf', 'pp'] for i in tests]) > 0
except AssertionError:
self._uvts_cls_logger.exception(
"Assertion exception occurred. No valid value for tests! "
"Choose from ['kpss', 'adf', 'pp']. You can choose more than one."
)
sys.exit("STOP")
do_test = list(
compress(['kpss', 'adf', 'pp'],
[i in ['kpss', 'adf', 'pp'] for i in tests]))
return dict(
zip(
do_test,
list(
map(
lambda x: ndiffs(
self.ts_df['y'], test=x, alpha=alpha, max_d=max_d),
do_test))))
def acf_plots(self):
"""Generates autocorrelation plots"""
fig, axes = plt.subplots(3, 2, figsize=(20, 9), sharex=False)
#
axes[0, 0].plot(self.ts_df['y'])
axes[0, 0].set_title('Original Series')
plot_acf(self.ts_df['y'], ax=axes[0, 1])
# 1st Differencing
axes[1, 0].plot(self.ts_df['y'].diff())
axes[1, 0].set_title('1st Order Differencing')
plot_acf(self.ts_df['y'].diff().dropna(), ax=axes[1, 1])
# 2nd Differencing
axes[2, 0].plot(self.ts_df['y'].diff().diff())
axes[2, 0].set_title('2nd Order Differencing')
plot_acf(self.ts_df['y'].diff().diff().dropna(), ax=axes[2, 1])
#
plt.gcf().autofmt_xdate()
plt.grid(True)
plt.show()
def pacf_plots(self):
"""Generates partial correlation plots"""
fig, axes = plt.subplots(3, 2, figsize=(20, 9), sharex=False)
#
axes[0, 0].plot(self.ts_df['y'])
axes[0, 0].set_title('Original Series')
plot_pacf(self.ts_df['y'], ax=axes[0, 1])
# 1st Differencing
axes[1, 0].plot(self.ts_df['y'].diff())
axes[1, 0].set_title('1st Order Differencing')
# axes[0].set(ylim=(0, 5))
plot_pacf(self.ts_df['y'].diff().dropna(), ax=axes[1, 1])
# 2nd Differencing
axes[2, 0].plot(self.ts_df['y'].diff().diff())
axes[2, 0].set_title('2nd Order Differencing')
plot_pacf(self.ts_df['y'].diff().diff().dropna(), ax=axes[2, 1])
plt.gcf().autofmt_xdate()
plt.grid(True)
plt.show()
@abstractmethod
def ts_fit(self, suppress=True):
self.model_fit = None
raise NotImplementedError("You must override ts_fit!")
@abstractmethod
def ts_test(self, show_plot=True):
raise NotImplementedError("You must override ts_test!")
def measure_rmse(self):
"""Computes root mean squared error on test data
"""
try:
assert self.residuals_forecast is not None
except AssertionError:
self._uvts_cls_logger.exception(
"AssertionError occurred, Cannot compute RMSE! Check your object mode"
)
self.rmse = np.sqrt(
sum(np.square(self.residuals_forecast)) /
len(self.residuals_forecast))
"""
if self._mode == 'test':
self.rmse_test = self.rmse
elif self._mode == 'test and validate':
self.rmse_val = self.rmse - self.rmse_test
elif self._mode == 'validate':
self.rmse_val = self.rmse
"""
def ts_validate(self, suppress=True, show_plot=True):
"""Validates the model"""
if self._mode == 'forecast': # or self._val_dt is None:
self._uvts_cls_logger.warning(
"Nothing to validate! n_val not set within the initialization or you already "
"used ts_forecast. In this case you have to restart and call ts_fit()."
)
sys.exit("STOP")
self._mode = 'test and validate'
self.ts_fit(suppress=suppress)
self.ts_test(show_plot=show_plot)
def reset(self):
for attr in self.__dict__.keys():
setattr(self, attr, None)
def main():
args = get_input_args()
main_logger = Logger("auto_arima app")
try:
assert args.dt_path.split('.')[-1] == 'csv'
except AssertionError:
main_logger.exception("Here only csv supported!")
sys.exit("STOP")
try:
ts_df = pd.read_csv(args.dt_path, index_col='Date', delimiter=',', usecols=['Date', args.value_column],
parse_dates=True)
main_logger.info("Data of shape {0} read in.".format(str(ts_df.shape)))
except IOError:
main_logger.exception("File could not be read!")
sys.exit("STOP")
except (NameError, KeyError):
main_logger.exception("Incompatible file format! Expected columns 'Date' and "+ args.value_column)
sys.exit("STOP")
# initiate
tsf_obj = AutoARIMAForecaster(ts_df=ts_df,
time_format=args.time_format,
freq=args.freq,
n_test=args.n_test,
n_val=args.n_val)
if args.transform != '':
tsf_obj.ts_transform(args.transform)
if input("Continue with ts_fit y/n?").strip().lower() == 'y':
tsf_obj.ts_fit(suppress=args.suppress)
else:
main_logger.info("OK")
if input("Continue with ts_diagnose y/n?").strip().lower() == 'y':
tsf_obj.ts_diagnose()
else:
main_logger.info("OK")
if input("Continue with ts_test y/n?").strip().lower() == 'y':
tsf_obj.ts_test()
else:
main_logger.info("OK")
if input("Continue with ts_forecast y/n?").strip().lower() == 'y':
tsf_obj.ts_forecast(n_forecast=args.n_forecast)
else:
main_logger.info("OK")
class EnsembleForecaster(LinearForecaster, SARIMAForecaster,
ExponentialSmoothingForecaster, AutoARIMAForecaster,
ProphetForecaster, DLMForecaster):
"""Univariate time series class inheriting from all existing forecasters and choosing the best ensemble.
Each forecaster is supposed to be equipped with y set of hyper parameters. Grid search is used to choose the
best model for each forecaster among the respective hyper parameters.
Those best models are then combined (Ensemble is created) to achieve the forecast of the best quality:
All combinations of best models are created, forecasted values for all these combinations are either
averaged or a median is computed. The best combination is chosen as the best ensemble.
Note, that it is necessary that test and validation data are generated.
Attributes
----------
_model_list: list
Internal (immutable) list of possible models
ensemble: list
List of all forecasters to be used to create the best ensemble.
_dict_models: dictionary
Dictionary keeping all models
dict_hyper_params: dictionary
Dictionary of hyper parameters per forecaster
show_plots: bool
Whether to show plots when models are fitted/tested
_best_models: dictionary
Dictionary keeping best models for each forecaster type in the list 'ensemble'.
This best one is chosen after applying the grid search.
best_ensemble: dictionary
Dictionary keeping the results of ensemble
_ensemble_logger: Logger
The logger for logging
Methods
----------
assertions()
Assertion tests, must be overrided
ts_fit()
Grid search on all forecasters in ensemble. Respective hyper parameters are used.
ts_test()
Test all forecasters on test data and computes rmse
ts_validate()
Validate all forecasters on validation data
_build_ensemble()
Builds the ensemble. All combinations of forecasters in ensemble is generated.
For each combination the meand and median rmse over the validation data is computed.
The best combination in terms of the best rmse is the best ensemble.
"""
def __init__(self,
dict_hyper_params,
ensemble=['dlm', 'prophet'],
show_plots=True,
**kwds):
"""Initialized the object EnsembleForecaster"""
self._model_list = [
'arima', 'sarima', 'exponential smoothing', 'prophet', 'dlm',
'linear'
]
self.ensemble = list(map(lambda x: x.lower(), ensemble))
self.dict_hyper_params = dict_hyper_params
self.show_plots = show_plots
self._dict_models = dict() # dict.fromkeys(self.ensemble, None)
self._best_models = dict()
self.best_ensemble = dict()
self._ensemble_logger = Logger("ensemble")
try:
super(EnsembleForecaster, self).__init__(**kwds)
except (TypeError, AttributeError) as e:
self._ensemble_logger.exception("Arguments missing...{}".format(e))
self._id = 'Ensemble'
#
if 'prophet' in self.ensemble:
self._dict_models['prophet'] = self.__copy__()
self._dict_models['prophet'].__class__ = ProphetForecaster
if 'linear' in self.ensemble:
self._dict_models['linear'] = self.__copy__()
self._dict_models['linear'].__class__ = LinearForecaster
if 'arima' in self.ensemble:
self._dict_models['arima'] = self.__copy__()
self._dict_models['arima'].__class__ = ARIMAForecaster
if 'sarima' in self.ensemble:
self._dict_models['sarima'] = self.__copy__()
self._dict_models['sarima'].__class__ = SARIMAForecaster
if 'exponential smoothing' in self.ensemble:
self._dict_models['expsm'] = self.__copy__()
self._dict_models[
'expsm'].__class__ = ExponentialSmoothingForecaster
if 'dlm' in self.ensemble:
self._dict_models['dlm'] = self.__copy__()
self._dict_models['dlm'].__class__ = DLMForecaster
if 'auto_arima' in self.ensemble:
self._dict_models['auto_arima'] = self.__copy__()
self._dict_models['auto_arima'].__class__ = AutoARIMAForecaster
if 'all' in self.ensemble:
self._dict_models['prophet'] = self.__copy__()
self._dict_models['prophet'].__class__ = ProphetForecaster
self._dict_models['linear'] = self.__copy__()
self._dict_models['linear'].__class__ = LinearForecaster
self._dict_models['arima'] = self.__copy__()
self._dict_models['arima'].__class__ = ARIMAForecaster
self._dict_models['sarima'] = self.__copy__()
self._dict_models['sarima'].__class__ = SARIMAForecaster
self._dict_models['expsm'] = self.__copy__()
self._dict_models[
'expsm'].__class__ = ExponentialSmoothingForecaster
self._dict_models['dlm'] = self.__copy__()
self._dict_models['dlm'].__class__ = DLMForecaster
self._dict_models['auto_arima'] = self.__copy__()
self._dict_models['auto_arima'].__class__ = AutoARIMAForecaster
self.assertions()
def assertions(self):
try:
assert isinstance(self.dict_hyper_params, dict)
except AssertionError:
self._ensemble_logger.exception(
"Assertion exception occurred, dict expected")
sys.exit("STOP")
#
"""
len_keys = list(filter(lambda x: x in list(self.dict_hyper_params.keys()),
keys_f(keys=self.ensemble)))
try:
assert len(len_keys) == len(self.ensemble)
except AssertionError:
self._dlm_logger.warning("hyper parameters found only for " + len_keys + " our of " + len(self.ensemble))
"""
for k, v in self._dict_models.items():
try:
assert self._dict_models[k].n_test > 0 and self._dict_models[
k].n_val > 0
except AssertionError:
self._ensemble_logger.exception(
"Assertion exception occurred, both test and validation "
"have to be generated! Please specify n_test and n_val!")
sys.exit("STOP")
def __copy__(self):
"""Copies the object"""
result = super(EnsembleForecaster, self).__copy__()
#
result.ensemble = self.ensemble
result.dict_hyper_params = self.dict_hyper_params
result._dict_models = self._dict_models
result._best_models = self._best_models
result._ensemble_logger = self._ensemble_logger
result._model_list = self._model_list
return result
def ts_fit(self, suppress=False):
"""Grid search on all forecasters in ensemble to find the best model out of hyper parameters provided.
Parameters:
----------
suppress: bool
Suppress or not some of the output messages
"""
for k, v in self._dict_models.items():
if k in list(self.dict_hyper_params.keys()):
self._gs.set_forecaster(self._dict_models[k])
self._gs.set_hyper_params(self.dict_hyper_params[k])
self._ensemble_logger.info(
"==========================================Starting grid search for the forecaster +++ {} +++ =================================="
.format(k))
self._gs = self._gs.grid_search(suppress=suppress,
show_plot=self.show_plots)
#
self._best_models[k] = self._gs.best_model
else:
self._dict_models[k].ts_fit(suppress=suppress)
if k not in list(self._best_models.keys()):
self._best_models[k] = dict()
self._best_models[k]['forecaster'] = self._dict_models[k]
return self
def ts_test(self, show_plot=True):
"""Test all models on test data
Parameters:
----------
show_plot: bool
Whether to show or not the residual plots
"""
for k, v in self._best_models.items():
self._ensemble_logger.info(
"==========================================Testing model +++ {} +++ ================================== "
.format(k))
if 'hyper_params' in list(self._best_models[k].keys()):
self._best_models[k]['forecaster'].set_params(
p_dict=self._best_models[k]['hyper_params'])
self._best_models[k]['forecaster'].ts_test(show_plot=show_plot)
self._build_ensemble()
self._plot_ensemble()
def plot_residuals(self):
"""Plot the residuals"""
if self._best_models is None or len(self._best_models) == 0:
self._ensemble_logger.warning(
"No models have been fit. The forecaster will stop!")
sys.exit("STOP")
for k, v in self._best_models.items():
self._best_models[k]['forecaster'].plot_residuals()
def ts_diagnose(self):
"""Plot the residuals"""
if self._best_models is None or len(self._best_models) == 0:
self._ensemble_logger.warning(
"No models have been fit. The forecaster will stop!")
sys.exit("STOP")
for k, v in self._best_models.items():
self._best_models[k]['forecaster'].ts_diagnose()
@staticmethod
def _print_dict(d):
e_info = ""
for k, v in d.items():
e_info = e_info + "....................... | ensemble | INFO : " + str(
k) + " : " + str(v) + "\n"
return "Best ensemble: \n" + e_info
@staticmethod
def lambda_forecast(x):
if isinstance(x, ProphetForecaster):
return x.forecast.iloc[:, -1].values
else:
return x.forecast.values
def _compute_ensemble(self, compute_rmse=False):
"""Re-computes 'ensemble_forecast' for best_ensemble"""
if self.best_ensemble['aggregation'] == 'none':
self.best_ensemble['ensemble_forecast'] = pd.Series(
self.lambda_forecast(self.best_ensemble['models'][0]),
index=self.best_ensemble['models'][0].forecast.index)
elif self.best_ensemble['aggregation'] == 'mean':
self.best_ensemble['ensemble_forecast'] = \
pd.Series(np.mean(list(map(lambda x: self.lambda_forecast(x), self.best_ensemble['models'])), axis=0),
index=self.best_ensemble['models'][0].forecast.index)
# rmse
if compute_rmse:
ensemble_res_mean = np.mean(list(
map(lambda x: x.residuals_forecast,
self.best_ensemble['models'])),
axis=0)
self.best_ensemble['rmse'] = np.sqrt(
np.square(ensemble_res_mean)).mean()
elif self.best_ensemble['aggregation'] == 'median':
self.best_ensemble['ensemble_forecast'] = \
pd.Series(np.median(list(map(lambda x: self.lambda_forecast(x), self.best_ensemble['models'])), axis=0),
index=self.best_ensemble['models'][0].forecast.index)
if compute_rmse:
ensemble_res_median = np.median(list(
map(lambda x: x.residuals_forecast,
self.best_ensemble['models'])),
axis=0)
self.best_ensemble['rmse'] = np.sqrt(
np.square(ensemble_res_median)).mean()
def _build_ensemble(self):
"""
# check that validation has been run
for k, v in self._best_models.items():
if self._best_models[k]['forecaster']._mode != 'test and validate':
# do what ts_validate does
self._best_models[k]['forecaster'].set_params(p_dict=self._best_models[k]['hyper_params'])
self._ensemble_logger.info("Validating model {}".format(k))
self._best_models[k]['forecaster'].ts_validate(suppress=suppress, show_plot=show_plot)
else:
pass
"""
# build ensemble
self._ensemble_logger.info(
"==========================================Start building the best ensemble=========================================="
)
rmse = np.float('Inf')
mod_list = list(self._best_models.keys())
for L in range(0, len(mod_list) + 1):
for subset in itertools.combinations(mod_list, L):
if len(subset) == 0:
pass
if len(subset) > 1:
#
ensemble_candidate = [
self._best_models[s]['forecaster'] for s in subset
]
# mean: note, residuals_forecast is now (each time) over the validation data
ensemble_res_mean = np.mean(list(
map(lambda x: x.residuals_forecast,
ensemble_candidate)),
axis=0)
if np.sqrt(np.square(ensemble_res_mean)).mean() < rmse:
rmse = np.sqrt(np.square(ensemble_res_mean)).mean()
self.best_ensemble['rmse'] = rmse
self.best_ensemble['set'] = subset
self.best_ensemble['models'] = ensemble_candidate
self.best_ensemble['aggregation'] = 'mean'
# median
ensemble_res_median = np.median(list(
map(lambda x: x.residuals_forecast,
ensemble_candidate)),
axis=0)
if np.sqrt(np.square(ensemble_res_median)).mean() < rmse:
rmse = np.sqrt(np.square(ensemble_res_median)).mean()
self.best_ensemble['rmse'] = rmse
self.best_ensemble['set'] = subset
self.best_ensemble['models'] = ensemble_candidate
self.best_ensemble['aggregation'] = 'median'
elif len(subset) == 1:
ensemble_candidate = self._best_models[
subset[0]]['forecaster']
if ensemble_candidate.rmse < rmse:
rmse = ensemble_candidate.rmse
self.best_ensemble['rmse'] = rmse
self.best_ensemble['set'] = subset
self.best_ensemble['models'] = [ensemble_candidate]
self.best_ensemble['aggregation'] = 'none'
self._compute_ensemble()
# self._ensemble_logger.info("The best ensemble found as:")
print(self._print_dict(self.best_ensemble))
def _plot_ensemble(self):
"""Plots the best ensemble"""
if len(self.best_ensemble) == 0:
self._ensemble_logger.error(
"Ensemble does not exist yet! Forecaster will stop!")
sys.exit("STOP")
plt.figure(figsize=(20, 7))
#
plt.plot(self.best_ensemble['models'][0].ts_df, color='b')
# colours
colors = mcolors.BASE_COLORS
by_hsv = sorted(
(tuple(mcolors.rgb_to_hsv(mcolors.to_rgb(color))), colo_name)
for colo_name, color in colors.items())
colo_names = [name for hsv, name in by_hsv]
if 'w' in colo_names:
colo_names.remove('w')
if 'b' in colo_names:
colo_names.remove('b')
if 'g' in colo_names:
colo_names.remove('g')
if 'darkgreen' in colo_names:
colo_names.remove('darkgreen')
colo_names = sample(colo_names, len(self.best_ensemble['models']))
#
for i in range(len(self.best_ensemble['models'])):
plt.plot(pd.Series(
self.lambda_forecast(self.best_ensemble['models'][i]),
index=self.best_ensemble['models'][i].forecast.index),
color=colo_names[i],
linewidth=2.0,
label=str(type(self.best_ensemble['models'][i])).split(
"'")[1].split('.')[2])
plt.plot(self.best_ensemble['ensemble_forecast'],
color='darkgreen',
linewidth=2.0,
label='Ensemble')
plt.axvline(x=min(self.best_ensemble['ensemble_forecast'].index),
color='grey',
linestyle='dashed')
plt.legend()
plt.title("Real (blue) and forecasted values")
#
plt.gcf().autofmt_xdate()
plt.grid(True)
plt.show()
def ts_validate(self, suppress=True, show_plot=True):
"""Validate best ensemble."""
if self.best_ensemble is None or len(self.best_ensemble) == 0:
self._ensemble_logger.error(
"Ensemble has not been built! Forecaster will stop!")
sys.exit("STOP")
for i in range(len(self.best_ensemble['models'])):
self.best_ensemble['models'][i]._mode = 'test and validate'
self.best_ensemble['models'][i].ts_fit(suppress=suppress)
self.best_ensemble['models'][i].ts_test(show_plot=show_plot)
self._compute_ensemble(compute_rmse=True)
print(self._print_dict(self.best_ensemble))
self._plot_ensemble()
def ts_forecast(self, n_forecast, features_dict=None, suppress=False):
if self.best_ensemble is None or len(self.best_ensemble) == 0:
self._ensemble_logger.error(
"Ensemble has not been built! Forecaster will stop!")
sys.exit("STOP")
for i in range(len(self.best_ensemble['models'])):
if str(type(self.best_ensemble['models'][i])).split("'")[1].split(
'.')[2] != 'DLMForecaster':
self.best_ensemble['models'][i].ts_forecast(
n_forecast=n_forecast, suppress=suppress)
else:
self.best_ensemble['models'][i].ts_forecast(
n_forecast=n_forecast,
features_dict=features_dict,
suppress=suppress)
self._compute_ensemble()
self._plot_ensemble()
class LinearForecaster(UVariateTimeSeriesClass):
"""Univariate time series child class using LinearRegression for forecasting
Attributes
----------
_fit_intercept: bool
Whether to fit the intercept yes/no
_normalize: bool
Whether to normalize time series data before fitting yes/no
_copy_X: bool
If True, X will be copied; else, it may be overwritten.
_n_jobs: int or None
The number of jobs to use for the computation. This will only provide speedup for n_targets > 1 and
sufficient large problems. None means 1 unless in a joblib.parallel_backend context.
-1 means using all processors.
Methods
----------
assertions()
Assertion tests, must be overrided
set_params()
Sets new parameter values
get_params_dict()
Gets parameter values as a dictionary
ts_fit()
Fits the auto_arima model to time series
ts_diagnose()
Diagnoses the fitted model
plot_residuals()
Generates residual plots
ts_test()
Evaluates fitted model on the test data, if this one has been generated
ts_forecast()
Forecasts time series and plots the results
plot_forecasts()
Plots forecasted time-series
"""
def __init__(self,
fit_intercept=True,
normalize=False,
copy_X=False,
n_jobs=None,
**kwds):
"""Initializes the object LinearForecaster"""
self._lin_logger = Logger('linear')
try:
super(LinearForecaster, self).__init__(**kwds)
except TypeError:
self._lin_logger.exception("Arguments missing...")
self._fit_intercept = fit_intercept
self._normalize = normalize
self._copy_X = copy_X
self._n_jobs = n_jobs
self.intercept = None
self.slope = None
self._id = 'Linear'
def __copy__(self):
"""Copies the object"""
result = super(LinearForecaster, self).__copy__()
result._fit_intercept = self._fit_intercept
result._normalize = self._normalize
result._copy_X = self._copy_X
result._n_jobs = self._n_jobs
result.intercept = self.intercept
result.slope = self.slope
result._lin_logger = self._lin_logger
return result
def set_params(self, p_dict=None, **kwargs):
"""Sets new parameters"""
params_dict = kwargs
if p_dict is not None:
params_dict = p_dict
#
for k, v in params_dict.items():
if k == 'ts_df':
self.ts_df = v
elif k == 'freq':
self.freq = v
elif k == 'n_test':
self.n_test = v
elif k == 'n_val':
self.n_val = v
elif k == 'timeformat':
self.time_format = v
elif k == 'fit_intercept':
self._fit_intercept = v
elif k == 'normalize':
self._normalize = v
elif k == 'copy_X':
self._copy_X = v
elif k == 'n_jobs':
self._n_jobs = v
return self
def get_params_dict(self):
"""Gets parameters as dictionary"""
return {'fit_intercept': self._fit_intercept,
'normalize': self._normalize,
'copy_X': self._copy_X,
'n_jobs': self._n_jobs
}
def ts_fit(self, suppress=False):
"""Fit LinearRegression to the time series data.
Parameters:
----------
suppress: bool
Suppress or not some of the output messages
"""
self._prepare_fit()
self.ts_split()
ts_df = self._train_dt.copy()
#
x = np.arange(0, len(ts_df)).reshape(-1, 1)
y = np.asarray(ts_df['y'])
# Fit
self._lin_logger.info("Trying to fit the linear model....")
# tic
start = time()
try:
if not suppress:
self._lin_logger.info("...via using parameters")
print_attributes(self)
self.model_fit = LinearRegression(fit_intercept=self._fit_intercept,
normalize=self._normalize,
copy_X=self._copy_X,
n_jobs=self._n_jobs).fit(x, y)
# toc
self._lin_logger.info("Time elapsed: {} sec.".format(time() - start))
except (Exception, ValueError):
self._lin_logger.exception("LinearRegression error...")
else:
#
self._lin_logger.info("Model successfully fitted to the data!")
if not suppress:
self._lin_logger.info("R^2: {:f}".format(self.model_fit.score(x, y)))
#
self.intercept = self.model_fit.intercept_
self.slope = self.model_fit.coef_
# Fitted values
self._lin_logger.info("Computing fitted values and residuals...")
self.fittedvalues = pd.Series(self.model_fit.predict(x), index=ts_df.index)
# Residuals
super(LinearForecaster, self)._residuals()
self._lin_logger.info("Done.")
return self
def ts_diagnose(self):
"""Diagnoses the model"""
try:
assert self.model_fit is not None
except AssertionError:
self._lin_logger.exception("Model has to be fitted first! Please call ts_fit(...)")
sys.exit("STOP")
self.plot_residuals()
def plot_residuals(self):
"""Plot the residuals"""
fig, axis = super(LinearForecaster, self)._plot_residuals(y=np.asarray(self._train_dt['y']),
yhat=np.asarray(self.fittedvalues), _id="Linear")
plt.gcf().autofmt_xdate()
plt.grid(True)
plt.show()
def ts_test(self, show_plot=True):
"""Test the fitted model if test data available"""
if super(LinearForecaster, self)._check_ts_test() < 0:
return
n_forecast = len(self._test_dt)
self._lin_logger.info("Evaluating the fitted Linear model on the test data...")
x_future = np.arange(len(self._train_dt), len(self._train_dt) + n_forecast).reshape(-1, 1)
self.forecast = pd.Series(self.model_fit.predict(x_future), index=self._test_dt.index)
self.residuals_forecast = pd.Series(np.asarray(self._test_dt['y']) - np.asarray(self.forecast),
index=self._test_dt.index)
self.measure_rmse()
self._lin_logger.info("RMSE on test data: {}".format(self.rmse))
# plot
if show_plot:
self.plot_forecast()
return self
def ts_forecast(self, n_forecast, suppress=False):
"""Forecast time series over time frame in the future specified via n_forecast"""
#
n_forecast = super(LinearForecaster, self)._check_ts_forecast(n_forecast)
#
if not suppress:
self._lin_logger.info("Fitting using all data....")
self._mode = 'forecast'
self.ts_fit(suppress=suppress)
self._lin_logger.info("Forecasting next " + str(n_forecast) + str(self.freq))
#
x_future = np.arange(len(self._train_dt), len(self._train_dt) + n_forecast).reshape(-1, 1)
future = self.model_fit.predict(x_future)
idx_future = self._gen_idx_future(n_forecast=n_forecast)
self.forecast = pd.Series(future, index=idx_future)
self.residuals_forecast = None
self.plot_forecast()
return self
def plot_forecast(self):
"""Plot forecasted values"""
fig, axis = super(LinearForecaster, self)._plot_forecast(y=np.asarray(self._train_dt['y']),
yhat=np.asarray(self.fittedvalues),
forecast=self.forecast, _id='Linear')
plt.gcf().autofmt_xdate()
plt.grid(True)
plt.show()
def __init__(self, forecasters=['all'], **kwds):
"""Initialized the object UVariateTimeSeriesForecaster"""
self.forecasters = list(map(lambda x: x.lower(), forecasters))
self._dict_models = dict() # .fromkeys(self._model_list, None)
self.best_model = None
self._uvtsf_logger = Logger("uvtsf")
#
try:
super(UVariateTimeSeriesForecaster, self).__init__(**kwds)
except TypeError:
self._uvtsf_logger.exception("Arguments missing...")
self._id = 'ts_forecaster'
#
if 'prophet' in self.forecasters:
self._dict_models['prophet'] = self.__copy__()
self._dict_models['prophet'].__class__ = ProphetForecaster
if 'linear' in self.forecasters:
self._dict_models['linear'] = self.__copy__()
self._dict_models['linear'].__class__ = LinearForecaster
if 'arima' in self.forecasters:
self._dict_models['arima'] = self.__copy__()
self._dict_models['arima'].__class__ = ARIMAForecaster
if 'sarima' in self.forecasters:
self._dict_models['sarima'] = self.__copy__()
self._dict_models['sarima'].__class__ = SARIMAForecaster
if 'auto_arima' in self.forecasters:
self._dict_models['auto_arima'] = self.__copy__()
self._dict_models['auto_arima'].__class__ = AutoARIMAForecaster
if 'exponential smoothing' in self.forecasters:
self._dict_models['expsm'] = self.__copy__()
self._dict_models[
'expsm'].__class__ = ExponentialSmoothingForecaster
if 'dlm' in self.forecasters:
self._dict_models['dlm'] = self.__copy__()
self._dict_models['dlm'].__class__ = DLMForecaster
if 'all' in self.forecasters:
self._dict_models['prophet'] = self.__copy__()
self._dict_models['prophet'].__class__ = ProphetForecaster
self._dict_models['linear'] = self.__copy__()
self._dict_models['linear'].__class__ = LinearForecaster
self._dict_models['arima'] = self.__copy__()
self._dict_models['arima'].__class__ = ARIMAForecaster
self._dict_models['sarima'] = self.__copy__()
self._dict_models['sarima'].__class__ = SARIMAForecaster
self._dict_models['auto_arima'] = self.__copy__()
self._dict_models['auto_arima'].__class__ = SARIMAForecaster
self._dict_models['expsm'] = self.__copy__()
self._dict_models[
'expsm'].__class__ = ExponentialSmoothingForecaster
self._dict_models['dlm'] = self.__copy__()
self._dict_models['dlm'].__class__ = DLMForecaster
self.assertions()
class SARIMAForecaster(ARIMAForecaster):
"""Univariate time series child class for forecasting using SARIMA
Attributes
----------
_order: tuple
a tuple of p, d, q
_s_order: tuple
A tuple of seasonal components (P, D, Q, lag)
_sarima_logger: Logger
The logger for logging
_sarima_trend: str
A parameter for controlling a model of the deterministic trend as one of ‘n’,’c’,’t’,’ct’ for no trend,
constant, linear, and constant with linear trend, respectively.
Methods
----------
assertions()
Assertion tests, must be overrided
set_params()
Sets new parameter values
get_params_dict()
Gets parameter values as a dictionary
ts_fit()
Fits the auto_arima model to time series
ts_diagnose()
Diagnoses the fitted model
plot_residuals()
Generates residual plots
ts_test()
Evaluates fitted model on the test data, if this one has been generated
ts_forecast()
Forecasts time series and plots the results
plot_forecasts()
Plots forecasted time-series
"""
def __init__(self, s_order=(1, 0, 1, 1), **kwds):
"""Initializes the object SARIMAForecaster"""
self._sarima_logger = Logger("SARIMA")
self._s_order = s_order
self._sarima_trend = ''
try:
super(SARIMAForecaster, self).__init__(**kwds)
except TypeError as e:
self._sarima_logger.exception("Arguments missing...{}".format(e))
self._model = None
self._init_trend()
self.assertions()
self._id = 'SARIMA'
def _init_trend(self):
if self._trend == 'constant':
self._sarima_trend = 'c'
elif self._trend is None:
self._srima_trend = 'n'
elif self._trend == 'linear':
self._sarima_trend = 't'
elif self._trend == 'constant linear':
self._sarima_trend = 'ct'
elif self._trend in ['additive', 'add']:
# self._sarima_logger.warningg("The trend " + str(self._trend) + " is not supported by SARIMA! "
# "Assuming linear trend")
self._sarima_trend = 't'
elif self._trend in ['multiplicative', 'mul']:
# self._sarima_logger.warning(
# "The trend " + str(self._trend) + " is not supported by ARIMA! Assuming linear trend")
self._sarima_trend = 't'
def assertions(self):
try:
assert isinstance(self._s_order, tuple)
except AssertionError:
self._sarima_logger.exception(
"Assertion exception occurred, tuple expected")
sys.exit("STOP")
try:
assert (self.hyper_params is not None
and len(self.hyper_params) != 0
and 'trend' in list(self.hyper_params.keys())) or (
self._sarima_trend is None
or self._sarima_trend in ['n', 'c', 't', 'ct'])
except AssertionError:
self._sarima_logger.exception(
"Assertion Error, trend must be in ['n', 'c', 't', 'ct']")
sys.exit("STOP")
try:
assert isinstance(self._seasonal, bool)
except AssertionError:
self._sarima_logger.exception(
"Assertion Error, seasonal must be boolean True/False in SARIMA"
)
sys.exit("STOP")
def __copy__(self):
"""Copies the object"""
result = super(SARIMAForecaster, self).__copy__()
result._s_order = self._s_order
result._sarima_trend = self._sarima_trend
result._sarima_logger = self._sarima_logger
return result
def set_params(self, p_dict=None, **kwargs):
"""Sets new parameters"""
params_dict = kwargs
if p_dict is not None:
params_dict = p_dict
#
for k, v in params_dict.items():
if k == 'ts_df':
self.ts_df = v
elif k == 'freq':
self.freq = v
elif k == 'n_test':
self.n_test = v
elif k == 'n_val':
self.n_val = v
elif k == 'timeformat':
self.time_format = v
elif k == 's_order':
self._s_order = v
elif k == 'order':
self._order = v
elif k == 'test':
self._test = v
elif k == 'trend':
self._sarima_trend = v
self.assertions()
return self
def get_params_dict(self):
"""Gets parameters as a dictionary"""
return {
'order': self._order,
'test': self._test,
'trend': self._sarima_trend,
's_order': self._s_order,
}
def ts_fit(self, suppress=False):
"""Fit Seasonal ARIMA to the time series data.
Parameters:
----------
suppress: bool
Suppress or not some of the output messages
"""
if self.hyper_params is not None:
self._gs.set_forecaster(self)
self._gs.set_hyper_params(self.hyper_params)
# a very important command here to avoid endless loop
self.hyper_params = None
self._sarima_logger.info("***** Starting grid search *****")
self._gs = self._gs.grid_search(suppress=suppress, show_plot=False)
#
self.best_model = self._gs.best_model
self.__dict__.update(self.best_model['forecaster'].__dict__)
self._sarima_logger.info("***** Finished grid search *****")
else:
self._prepare_fit()
self.ts_split()
self._init_trend()
ts_df = self._train_dt.copy()
# Fit
self._sarima_logger.info("Trying to fit the sarima model....")
# tic
start = time()
try:
if not suppress:
self._sarima_logger.info("...via using parameters\n")
print_attributes(self)
self._model = SARIMAX(ts_df['y'],
order=self._order,
seasonal_order=self._s_order,
trend=self._sarima_trend,
enforce_stationarity=False,
enforce_invertibility=False,
freq=self.freq)
self.model_fit = self._model.fit(disp=1)
except (Exception, ValueError):
self._sarima_logger.exception(
"Exception occurred in the fit...")
self._sarima_logger.error("Please try other parameters!")
self.model_fit = None
else:
# toc
self._sarima_logger.info(
"Time elapsed: {} sec.".format(time() - start))
self._sarima_logger.info(
"Model successfully fitted to the data!")
if not suppress:
self._sarima_logger.info("The model summary: " +
str(self.model_fit.summary()))
# Fitted values
self._sarima_logger.info(
"Computing fitted values and residuals...")
self.fittedvalues = self.model_fit.fittedvalues
# prolong: for some reason this package returns fitted values this way
if len(self.fittedvalues) != len(self._train_dt):
self.fittedvalues = pd.DataFrame(
index=pd.date_range(ts_df.index[0],
ts_df.index[len(ts_df) - 1],
freq=self.freq),
columns=['dummy']).join(pd.DataFrame(
self.fittedvalues)).drop(['dummy'], axis=1)
self.fittedvalues = self.fittedvalues.reset_index()
self.fittedvalues.columns = self._ts_df_cols
self.fittedvalues.set_index('ds', inplace=True)
self.fittedvalues.y = self.fittedvalues.y.fillna(
method='bfill')
# Residuals
super(SARIMAForecaster, self)._residuals()
self._sarima_logger.info("Done.")
return self
def plot_residuals(self):
"""Plot the residuals"""
fig, axis = super(SARIMAForecaster, self)._plot_residuals(
y=np.asarray(self._train_dt['y']),
yhat=np.asarray(self.fittedvalues).flatten(),
_id="SARIMA")
plt.gcf().autofmt_xdate()
plt.grid(True)
plt.show()
def ts_test(self, show_plot=True):
"""Test the fitted model if test data available"""
if super(SARIMAForecaster, self)._check_ts_test() < 0:
return
n_forecast = len(self._test_dt)
if self._mode == 'test':
self._sarima_logger.info(
"Evaluating the fitted SARIMA model on the test data...")
elif self._mode == 'test and validate':
self._sarima_logger.info(
"Evaluating the fitted SARIMA model on the test and validation data..."
)
future = self.model_fit.predict(start=len(self._train_dt.index),
end=len(self._train_dt.index) +
n_forecast - 1,
dynamic=True)
self.forecast = pd.Series(future, index=self._test_dt.index)
self.residuals_forecast = pd.Series(np.asarray(self._test_dt.y) -
np.asarray(self.forecast),
index=self._test_dt.index)
self.measure_rmse()
self._sarima_logger.info("RMSE on test data: {}".format(self.rmse))
# plot
if show_plot:
self.plot_forecast()
return self
def ts_forecast(self, n_forecast, suppress=False):
"""Forecast time series over time frame in the future specified via n_forecast"""
#
n_forecast = super(SARIMAForecaster,
self)._check_ts_forecast(n_forecast)
#
self._sarima_logger.info("Fitting using all data....")
self._mode = 'forecast'
self.ts_fit(suppress=suppress)
self._sarima_logger.info("Forecasting next " + str(n_forecast) +
str(self.freq))
#
future = self.model_fit.predict(start=len(self._train_dt.index),
end=len(self._train_dt.index) +
(n_forecast - 1),
dynamic=True)
idx_future = self._gen_idx_future(n_forecast=n_forecast)
self.forecast = pd.Series(future, index=idx_future)
# self.forecast = future
self.residuals_forecast = None
self.plot_forecast()
return self
def plot_forecast(self):
"""Plot forecasted values"""
fig, axis = super(SARIMAForecaster, self)._plot_forecast(
y=np.asarray(self._train_dt['y']),
yhat=np.asarray(self.fittedvalues).flatten(),
forecast=self.forecast,
_id='SARIMA')
plt.gcf().autofmt_xdate()
plt.grid(True)
plt.show()
class ExponentialSmoothingForecaster(UVariateTimeSeriesClass):
"""Univariate time series child class using simple, double or triple exponential smoothing for forecasting
Attributes
----------
ref. to e.g., https://machinelearningmastery.com/exponential-smoothing-for-time-series-forecasting-in-python/
_optimized: bool
Whether to optimize smoothing coefficients
_smoothing_level: float
(alpha): the smoothing coefficient for the level
_es_trend: str
The type of trend component, as either “add” for additive or “mul” for multiplicative.
Modeling the trend can be disabled by setting it to None
_damped: bool
Whether or not the trend component should be damped, either True or False
_es_seasonal: str
The type of seasonal component, as either “add” for additive or “mul” for multiplicative.
Modeling the seasonal component can be disabled by setting it to None
_seasonal_periods: int
The number of time steps in a seasonal period, e.g. 12 for 12 months in a yearly seasonal structure
_smoothing_slope: float
(beta): the smoothing coefficient for the trend
_smoothing_seasonal: float
(gamma): the smoothing coefficient for the seasonal component
_damping_slope: float
(phi): the coefficient for the damped trend
_use_boxcox: {True, False, ‘log’, float}
Should the Box-Cox transform be applied to the data first? If ‘log’ then apply the log.
If float then use lambda equal to float
_remove_bias: bool
Remove bias from forecast values and fitted values by enforcing that the average residual is equal to zero.
_use_brute: bool
Search for good starting values using a brute force (grid) optimizer.
If False, a naive set of starting values is used.
_expsm_logger: Logger
The logger for logging
Methods
----------
assertions()
Assertion tests, must be overrided
set_params()
Sets new parameter values
get_params_dict()
Gets parameter values as a dictionary
ts_fit()
Fits the auto_arima model to time series
ts_diagnose()
Diagnoses the fitted model
plot_residuals()
Generates residual plots
ts_test()
Evaluates fitted model on the test data, if this one has been generated
ts_forecast()
Forecasts time series and plots the results
plot_forecasts()
Plots forecasted time-series
"""
def __init__(self,
smoothing_level=None,
optimized=False,
damped=False,
smoothing_slope=None,
smoothing_seasonal=None,
damping_slope=None,
use_boxcox=False,
remove_bias=False,
use_brute=False,
**kwds):
"""Initializes the object ExponentialSmoothingForecaster"""
self._expsm_logger = Logger("ExpSmoothing")
self._es_trend = None
self._es_seasonal = None
try:
super(ExponentialSmoothingForecaster, self).__init__(**kwds)
except TypeError:
self._expsm_logger.exception("Arguments missing...")
self._init_trend()
self._init_seasonal()
self._smoothing_level = smoothing_level
self._optimized = optimized
self._damped = damped
self._smoothing_slope = smoothing_slope
self._smoothing_seasonal = smoothing_seasonal
self._damping_slope = damping_slope
self._use_boxcox = use_boxcox
self._remove_bias = remove_bias
self._use_brute = use_brute
self.assertions()
self._id = 'ExponentialSmoothing'
def _init_trend(self):
if self._trend is None or self._trend == 'constant':
self._es_trend = None
elif self._trend in ['linear', 'constant linear']:
# self._expsm_logger.warning("The trend " + self(self._trend) + " not supported in Exponential Smoothing! "
# "Assuming additive trend")
self._es_trend = 'add'
else:
self._es_trend = self._trend
def _init_seasonal(self):
if isinstance(self._seasonal, bool):
if self._seasonal:
# self._expsm_logger.warning("Assuming additive seasonal component in Exponential Smoothing")
self._es_seasonal = 'add'
else:
self._es_seasonal = None
else:
self._es_seasonal = self._seasonal
def __copy__(self):
"""Copies the object"""
result = super(ExponentialSmoothingForecaster, self).__copy__()
result._smoothing_level = self._smoothing_level
result._optimized = self._optimized
result._es_trend = self._es_trend
result._es_seasonal = self._es_seasonal
result._damped = self._damped
result._smoothing_slope = self._smoothing_slope
result._smoothing_seasonal = self._smoothing_seasonal
result._damping_slope = self._damping_slope
result._use_boxcox = self._use_boxcox
result._remove_bias = self._remove_bias
result._use_brute = self._use_brute
result._expsm_logger = self._expsm_logger
return result
def assertions(self):
try:
assert (self.hyper_params is not None
and len(self.hyper_params) != 0
and 'trend' in list(self.hyper_params.keys())) or (
self._es_trend is None or self._es_trend
in ['add', 'mul', 'additive', 'multiplicative'])
except AssertionError:
self._expsm_logger.exception(
"Assertion Error, trend must be in ['add','mul',"
"'additive','multiplicative']")
sys.exit("STOP")
try:
assert self._es_seasonal is None or isinstance(
self._es_seasonal, str) and self._es_seasonal in [
'add', 'mul', 'additive', 'multiplicative'
]
except AssertionError:
self._expsm_logger.exception(
"Assertion Error, seasonal must be in ['add','mul',"
"'additive','multiplicative']")
sys.exit("STOP")
def set_params(self, p_dict=None, **kwargs):
"""Sets new parameters"""
params_dict = kwargs
if p_dict is not None:
params_dict = p_dict
#
for k, v in params_dict.items():
if k == 'smoothing_level':
self._smoothing_level = v
elif k == 'optimized':
self._optimized = v
elif k == 'trend':
self._es_trend = v
elif k == 'seasonal':
self._es_seasonal = v
elif k == 'seasonal_periods':
self._seasonal_periods = v
elif k == 'damped':
self._damped = v
elif k == 'smoothing_slope':
self._smoothing_slope = v
elif k == 'smoothing_seasonal':
self._smoothing_seasonal = v
elif k == 'damping_slope':
self._damping_slope = v
elif k == 'use_boxcox':
self._use_boxcox = v
elif k == 'remove_bias':
self._remove_bias = v
elif k == 'use_brute':
self._use_brute = v
self.assertions()
return self
def get_params_dict(self):
"""Gets parameters as dictionary"""
return {
'smoothing_level': self._smoothing_level,
'optimized': self._optimized,
'trend': self._es_trend,
'seasonal': self._es_seasonal,
'seasonal_periods': self._seasonal_periods,
'damped': self._damped,
'smoothing_slope': self._smoothing_slope,
'smoothing_seasonal': self._smoothing_seasonal,
'damping_slope': self._damping_slope,
'use_boxcox': self._use_boxcox,
'remove_bias': self._remove_bias,
'use_brute': self._use_brute
}
def ts_fit(self, suppress=False):
"""Fit Exponential Smoothing to the time series data.
Parameters:
----------
suppress: bool
Suppress or not some of the output messages
"""
if self.hyper_params is not None:
self._gs.set_forecaster(self)
self._gs.set_hyper_params(self.hyper_params)
# a very important command here to avoid endless loop
self.hyper_params = None
self._expsm_logger.info("***** Starting grid search *****")
self._gs = self._gs.grid_search(suppress=suppress, show_plot=False)
#
self.best_model = self._gs.best_model
self.__dict__.update(self.best_model['forecaster'].__dict__)
self._expsm_logger.info("***** Finished grid search *****")
else:
self._prepare_fit()
self.ts_split()
self._init_trend()
self._init_seasonal()
ts_df = self._train_dt.copy()
# Fit
print("Trying to fit the exponential smoothing model....")
# tic
start = time()
try:
if not suppress:
self._expsm_logger.info("...via using parameters\n")
print_attributes(self)
#
self.model_fit = ExponentialSmoothing(
ts_df,
freq=self.freq,
trend=self._es_trend,
seasonal=self._es_seasonal,
seasonal_periods=self._seasonal_periods,
damped=self._damped).fit(
smoothing_level=self._smoothing_level,
smoothing_slope=self._smoothing_slope,
smoothing_seasonal=self._smoothing_seasonal,
damping_slope=self._damping_slope,
optimized=self._optimized,
use_boxcox=self._use_boxcox,
remove_bias=self._remove_bias)
# toc
self._expsm_logger.info("Time elapsed: {} sec.".format(time() -
start))
except (Exception, ValueError):
self._expsm_logger.exception("Exponential Smoothing error...")
else:
#
self._expsm_logger.info(
"Model successfully fitted to the data!")
# Fitted values
self._expsm_logger.info(
"Computing fitted values and residuals...")
self.fittedvalues = self.model_fit.fittedvalues
# Residuals
super(ExponentialSmoothingForecaster, self)._residuals()
self._expsm_logger.info("Done.")
return self
def ts_diagnose(self):
"""Diagnose the model"""
try:
assert self.model_fit is not None
except AssertionError:
self._expsm_logger.exception(
"Model has to be fitted first! Please call ts_fit(...)")
sys.exit("STOP")
self.plot_residuals()
def plot_residuals(self):
"""Plot the residuals"""
fig, axis = super(ExponentialSmoothingForecaster,
self)._plot_residuals(
y=np.asarray(self._train_dt['y']),
yhat=np.asarray(self.fittedvalues),
_id="Exponential Smoothing")
plt.gcf().autofmt_xdate()
plt.grid(True)
plt.show()
def ts_test(self, show_plot=True):
"""Test the fitted model if test data available"""
if super(ExponentialSmoothingForecaster, self)._check_ts_test() < 0:
return
n_forecast = len(self._test_dt)
self._expsm_logger.info(
"Evaluating the fitted model on the test data...")
self.forecast = self.model_fit.forecast(n_forecast)
self.residuals_forecast = pd.Series(np.asarray(self._test_dt['y']) -
np.asarray(self.forecast),
index=self._test_dt.index)
self.measure_rmse()
self._expsm_logger.info("RMSE on test data: {}".format(self.rmse))
# plot
if show_plot:
self.plot_forecast()
def ts_forecast(self, n_forecast, suppress):
"""Forecast time series over time frame in the future specified via n_forecast"""
#
n_forecast = super(ExponentialSmoothingForecaster,
self)._check_ts_forecast(n_forecast)
#
self._expsm_logger.info("Fitting using all data....")
self._mode = 'forecast'
self.ts_fit(suppress=suppress)
self._expsm_logger.info("Forecasting next " + str(n_forecast) +
str(self.freq))
#
self.forecast = self.model_fit.forecast(n_forecast)
self.residuals_forecast = None
# self.plot_forecast()
return self
def plot_forecast(self):
"""Plot forecasted values"""
fig, axis = super(ExponentialSmoothingForecaster, self)._plot_forecast(
y=np.asarray(self._train_dt['y']),
yhat=np.asarray(self.fittedvalues),
forecast=self.forecast,
_id='Exponential Smoothing')
plt.gcf().autofmt_xdate()
plt.grid(True)
plt.show()
class ProphetForecaster(UVariateTimeSeriesClass):
"""Univariate time series child class using Prophet for forecasting,ref. to https://facebook.github.io/prophet
Attributes
----------
_prophet_interval_width: float
The width of the uncertainty intervals (by default 80%), also
ref. to https://facebook.github.io/prophet/docs/uncertainty_intervals.html
_yearly_seasonality: bool
Consider yearly seasonality yes/no
_monthly_seasonality: bool
Consider monthly seasonality yes/no
_quarterly_seasonality: bool
Consider quarterly seasonality yes/no
_weekly_seasonality:
Consider weekly seasonality yes/no
_daily_seasonality: bool
Consider daily seasonality yes/no
_weekend_seasonality: bool#
Consider week-end seasonality yes/no.
ref. to https://facebook.github.io/prophet/docs/seasonality,_holiday_effects,_and_regressors.html#modeling-holidays-and-special-events
_changepoint_prior_scale: float
If the trend changes are being overfit (too much flexibility) or underfit (not enough flexibility),
you can adjust the strength of the sparse prior using this argument.
By default, this parameter is set to 0.05. Increasing it will make the trend more flexible.
Decreasing it will make the trend less flexible.
ref. to https://facebook.github.io/prophet/docs/trend_changepoints.html#automatic-changepoint-detection-in-prophet
_changepoint_range: float
By default changepoints are only inferred for the first 80% of the time series in order to have plenty of runway
for projecting the trend forward and to avoid overfitting fluctuations at the end of the time series.
This default works in many situations but not all, and can be changed using the changepoint_range argument.
For example, m = Prophet(changepoint_range=0.9) will place potential changepoints in
the first 90% of the time series.
ref. to https://facebook.github.io/prophet/docs/trend_changepoints.html#automatic-changepoint-detection-in-prophet
_add_change_points: bool
Whether to add change points to the plots
ref. to https://facebook.github.io/prophet/docs/trend_changepoints.html#automatic-changepoint-detection-in-prophet
_diagnose: bool
Whether to run cross validation yes/no
_history: str
Amount of historic data in days for cross validation,
Corresponds to initial in https://facebook.github.io/prophet/docs/diagnostics.html
_step: str
Correspons to period in the linke above. Defines step in days to shift the historic data
_horizon: str
Forecasting horizon in days for each cross validation run
_consider_holidays: bool
Whether to consider holiodays yes/no
ref. to https://facebook.github.io/prophet/docs/seasonality,_holiday_effects,_and_regressors.html#modeling-holidays-and-special-events
_country: str
The country for which holidays are to be considered
_prophet_logger: Logger
The logger for logging
Methods
----------
assertions()
Assertion tests, must be overrided
set_params()
Sets new parameter values
get_params_dict()
Gets parameter values as a dictionary
ts_fit()
Fits the auto_arima model to time series
ts_diagnose()
Diagnoses the fitted model. Cross validation is started
plot_residuals()
Generates residual plots
ts_test()
Evaluates fitted model on the test data, if this one has been generated
ts_forecast()
Forecasts time series and plots the results
plot_forecasts()
Plots forecasted time-series
"""
def __init__(self,
prophet_interval_width=0.95,
yearly_seasonality=False,
monthly_seasonality=False,
quarterly_seasonality=False,
weekly_seasonality=False,
daily_seasonality=False,
weekend_seasonality=False,
changepoint_prior_scale=0.001,
changepoint_range=0.9,
add_change_points=True,
diagnose=False,
history=None,
step=None,
horizon=None,
consider_holidays=True,
country='DE',
**kwds):
"""Initializes the object ProphetForecaster"""
self._prophet_logger = Logger('prophet')
try:
super(ProphetForecaster, self).__init__(**kwds)
except TypeError:
self._prophet_logger.exception("TypeError occurred, Arguments missing")
self._model = None
self._prophet_interval_width = prophet_interval_width
self._yearly_seasonality = yearly_seasonality
self._monthly_seasonality = monthly_seasonality
self._quarterly_seasonality = quarterly_seasonality
self._weekly_seasonality = weekly_seasonality
self._daily_seasonality = daily_seasonality
self._weekend_seasonality = weekend_seasonality
self._changepoint_prior_scale = changepoint_prior_scale
self._changepoint_range = changepoint_range
self._add_change_points = add_change_points
self._diagnose = diagnose
self._history = history
self._step = step
self._horizon = horizon
self._prophet_cv = None
self._prophet_p = None
self._consider_holidays = consider_holidays
self._country = country
self._id = 'Prophet'
def __copy__(self):
"""Copies the object"""
result = super(ProphetForecaster, self).__copy__()
#
result._model = self._model
result._prophet_interval_width = self._prophet_interval_width
result._yearly_seasonality = self._yearly_seasonality
result._monthly_seasonality = self._monthly_seasonality
result._quarterly_seasonality = self._quarterly_seasonality
result._weekly_seasonality = self._weekly_seasonality
result._daily_seasonality = self._daily_seasonality
result._weekend_seasonality = self._weekend_seasonality
result._changepoint_prior_scale = self._changepoint_prior_scale
result._changepoint_range = self._changepoint_range
result._add_change_points = self._add_change_points
result._diagnose = self._diagnose
result._history = self._history
result._step = self._step
result._horizon = self._horizon
result._prophet_cv = self._prophet_cv
result._prophet_p = self._prophet_p
result._consider_holidays = self._consider_holidays
result._country = self._country
result._prophet_logger = self._prophet_logger
return result
def set_params(self, p_dict=None, **kwargs):
"""Sets new parameters"""
params_dict = kwargs
if p_dict is not None:
params_dict = p_dict
#
for k, v in params_dict.items():
if k == 'ts_df':
self.ts_df = v
elif k == 'freq':
self.freq = v
elif k == 'n_test':
self.n_test = v
elif k == 'n_val':
self.n_val = v
elif k == 'timeformat':
self.time_format = v
elif k == "prophet_interval_width":
self._prophet_interval_width = v
elif k == "yearly_seasonality":
self._yearly_seasonality = v
elif k == "monthly_seasonality":
self._monthly_seasonality = v
elif k == "quarterly_seasonality":
self._quarterly_seasonality = v
elif k == "weekly_seasonality":
self._weekly_seasonality = v
elif k == "daily_seasonality":
self._daily_seasonality = v
elif k == "weekend_seasonality":
self._weekend_seasonality = v
elif k == "changepoint_prior_scale":
self._changepoint_prior_scale = v
elif k == "changepoint_range":
self._changepoint_range = v
elif k == "add_change_points":
self._add_change_points = v
elif k == "diagnose":
self._diagnose = v
elif k == "history":
self._history = v
elif k == "step":
self._step = v
elif k == "horizon":
self._horizon = v
elif k == "consider_holidays":
self._consider_holidays = v
elif k == "country":
self._country = v
return self
def get_params_dict(self):
"""Gets parameters as a dictionary"""
return {'prophet_interval_width': self._prophet_interval_width,
'yearly_seasonality': self._yearly_seasonality,
'monthly_seasonality': self._monthly_seasonality,
'quarterly_seasonality': self._quarterly_seasonality,
'weekly_seasonality': self._weekly_seasonality,
'daily_seasonality': self._daily_seasonality,
'weekend_seasonality': self._weekend_seasonality,
'changepoint_prior_scale': self._changepoint_prior_scale,
'changepoint_range': self._changepoint_range,
'add_change_points': self._add_change_points,
'diagnose': self._diagnose,
'history': self._history,
'step': self._step,
'horizon': self._horizon,
'consider_holidays': self._consider_holidays,
'country': self._country
}
@staticmethod
def we_season(ds):
"""Lambda function to prepare weekend_seasonality for Prophet"""
date = pd.to_datetime(ds)
return date.weekday() >= 5
def ts_fit(self, suppress=False):
"""Fit Prophet to the time series data.
Parameters:
----------
suppress: bool
Suppress or not some of the output messages
"""
if self.hyper_params is not None:
self._gs.set_forecaster(self)
self._gs.set_hyper_params(self.hyper_params)
# a very important command here to avoid endless loop
self.hyper_params = None
self._prophet_logger.info("***** Starting grid search *****")
self._gs = self._gs.grid_search(suppress=suppress, show_plot=False)
#
self.best_model = self._gs.best_model
self.__dict__.update(self.best_model['forecaster'].__dict__)
self._prophet_logger.info("***** Finished grid search *****")
else:
self._prepare_fit()
self._model = None
self.ts_split()
ts_df = self._train_dt.copy()
ts_test_df = self._test_dt
# sanity check
if 'on_weekend' in ts_df.columns:
ts_df.drop(['on_weekend', 'off_weekend'], inplace=True, axis=1)
# ts_test_df.drop(['on_weekend', 'off_weekend'], inplace=True, axis=1)
# Fit
self._prophet_logger.info("Trying to fit the Prophet model....")
try:
if not suppress:
self._prophet_logger.info("...via using parameters\n")
print_attributes(self)
# diagnose on?
if self._diagnose:
try:
assert self._step is not None and self._horizon is not None
except (KeyError, AssertionError):
self._prophet_logger.warning("You want to diagnose the Prophet model. Please provide parameters "
"'step' and 'horizon' within object initialization!")
sys.exit("STOP")
ts_df = ts_df.reset_index()
ts_df.columns = self._ts_df_cols
if ts_test_df is not None and not ts_test_df.empty:
ts_test_df = ts_test_df.reset_index()
ts_test_df.columns = self._ts_df_cols
#
weekly_s = self._weekly_seasonality
if self._weekend_seasonality:
# force to False
weekly_s = False
#
if not self._consider_holidays:
self._model = Prophet(interval_width=self._prophet_interval_width,
yearly_seasonality=self._yearly_seasonality,
weekly_seasonality=weekly_s,
daily_seasonality=self._daily_seasonality,
changepoint_range=self._changepoint_range,
changepoint_prior_scale=self._changepoint_prior_scale)
else:
try:
assert self._country in ['AT', 'DE', 'US']
except AssertionError:
self._prophet_logger.exception("Assrtion exception occurred. Right now, Austria (AT), "
"Germany(DE) and USA (US) supported.")
sys.exit("STOP")
else:
holi = None
if self._country == 'AT':
holi = holidays.AT(state=None, years=list(np.unique(np.asarray(self.ts_df.index.year))))
elif self._country == 'DE':
holi = holidays.DE(state=None, years=list(np.unique(np.asarray(self.ts_df.index.year))))
elif self._country == 'US':
holi = holidays.US(state=None, years=list(np.unique(np.asarray(self.ts_df.index.year))))
#
holi_dict = dict()
for date, name in sorted(holi.items()):
holi_dict[date] = name
df_holi = pd.DataFrame.from_dict(data=holi_dict, orient='index').reset_index()
df_holi.columns = ['ds', 'holiday']
df_holi['lower_window'] = 0
df_holi['upper_window'] = 0
self._model = Prophet(interval_width=self._prophet_interval_width,
yearly_seasonality=self._yearly_seasonality,
weekly_seasonality=weekly_s,
daily_seasonality=self._daily_seasonality,
changepoint_range=self._changepoint_range,
changepoint_prior_scale=self._changepoint_prior_scale,
holidays=df_holi)
if self._monthly_seasonality:
self._model.add_seasonality(name='monthly', period=30.5, fourier_order=20)
if not suppress:
self._prophet_logger.info("Added monthly seasonality.")
if self._quarterly_seasonality:
self._model.add_seasonality(name='quarterly', period=91.5, fourier_order=20)
if not suppress:
self._prophet_logger.info("Added quarterly seasonality.")
if self._weekend_seasonality:
ts_df['on_weekend'] = ts_df['ds'].apply(self.we_season)
ts_df['off_weekend'] = ~ts_df['ds'].apply(self.we_season)
self._train_dt = ts_df.copy()
self._train_dt.set_index('ds', inplace=True)
#
if ts_test_df is not None and not ts_test_df.empty:
ts_test_df['on_weekend'] = ts_test_df['ds'].apply(self.we_season)
ts_test_df['off_weekend'] = ~ts_test_df['ds'].apply(self.we_season)
self._test_dt = ts_test_df.copy()
self._test_dt.set_index('ds', inplace=True)
# and add
self._model.add_seasonality(name='weekend_on_season', period=7,
fourier_order=5, condition_name='on_weekend')
self._model.add_seasonality(name='weekend_off_season', period=7,
fourier_order=5, condition_name='off_weekend')
if not suppress:
self._prophet_logger.info("Added week-end seasonality.")
# tic
start = time()
self.model_fit = self._model.fit(ts_df)
# toc
if not suppress:
self._prophet_logger.info("Time elapsed: {} sec.".format(time() - start))
except (Exception, ValueError):
self._prophet_logger.exception("Prophet error...")
return -1
else:
self._prophet_logger.info("Model successfully fitted to the data!")
# Fitted values
self._prophet_logger.info("Computing fitted values and residuals...")
# in-sample predict
try:
self.fittedvalues = self._model.predict(ts_df.drop('y', axis=1))
except (Exception, ValueError):
self._prophet_logger.exception("Prophet predict error...")
# Residuals
try:
# use fittedvalues to fill in the model dictionary
self.residuals = pd.Series(np.asarray(ts_df.y) - np.asarray(self.fittedvalues['yhat']),
index=self._train_dt.index)
except (KeyError, AttributeError):
self._prophet_logger.exception("Model was not fitted or ts has other structure...")
#
self.lower_conf_int = pd.Series(np.asarray(self.fittedvalues['yhat_lower']), index=self._train_dt.index)
self.upper_conf_int = pd.Series(np.asarray(self.fittedvalues['yhat_upper']), index=self._train_dt.index)
self._prophet_logger.info("Done.")
return self
def ts_diagnose(self):
"""Diagnoses the fitted model"""
try:
assert self.model_fit is not None
except AssertionError:
self._prophet_logger.exception("Model has to be fitted first! Please call ts_fit(...)")
sys.exit("STOP")
self.plot_residuals()
if self._diagnose:
if input("Run cross validation y/n? Note, depending on parameters provided "
"this can take some time...").strip().lower() == 'y':
start = time()
self._prophet_logger.info("Running cross validation using parameters provided....")
if self._history is not None:
try:
self._prophet_cv = cross_validation(self.model_fit, initial=self._history,
period=self._step,
horizon=self._horizon)
except Exception:
self._prophet_logger.exception("Prophet cross validation error: check your "
"parameters 'history', 'horizon', 'step'!")
else:
try:
self._prophet_cv = cross_validation(self.model_fit, period=self._step,
horizon=self._horizon)
except Exception:
self._prophet_logger.exception("Prophet cross validation error: "
"check your parameters 'horizon', 'step'!")
self._prophet_logger.info("Time elapsed: {}".format(time() - start))
simu_intervals = self._prophet_cv.groupby('cutoff')['ds'].agg(
[('forecast_start', 'min'),
('forecast_till', 'max')])
self._prophet_logger.info("Following time windows and cutoffs have been set-up:\n")
print(simu_intervals)
#
plot_cross_validation_metric(self._prophet_cv, metric='mape')
#
self._prophet_logger.info("Running performance metrics...")
self._prophet_p = performance_metrics(self._prophet_cv)
else:
self._prophet_logger.info("OK")
return
def plot_residuals(self):
"""Plot the residuals"""
fig, axes = super(ProphetForecaster, self)._plot_residuals(
y=np.asarray(self._train_dt['y']), yhat=np.asarray(self.fittedvalues['yhat']), _id="Prophet")
plt.gcf().autofmt_xdate()
plt.grid(True)
plt.show()
def ts_test(self, show_plot=True):
"""Test the fitted model if test data available"""
if super(ProphetForecaster, self)._check_ts_test() < 0:
return
self._prophet_logger.info("Evaluating the fitted Prophet model on the test data...")
self.forecast = self._model.predict(self._test_dt.copy().reset_index().drop('y', axis=1))
# confidence intervals
self.lower_conf_int = pd.concat([self.lower_conf_int,
pd.Series(np.asarray(self.forecast['yhat_lower']), index=self._test_dt.index)],
axis=0)
self.upper_conf_int = pd.concat([self.upper_conf_int,
pd.Series(np.asarray(self.forecast['yhat_upper']), index=self._test_dt.index)],
axis=0)
self.residuals_forecast = pd.Series(np.asarray(self._test_dt['y']) - np.asarray(self.forecast['yhat']),
index=self._test_dt.index)
self.measure_rmse()
self._prophet_logger.info("RMSE on test data: {}".format(self.rmse))
# plot
if show_plot:
self.plot_forecast()
def ts_forecast(self, n_forecast, suppress):
"""Forecast time series over time frame in the future specified via n_forecast"""
#
n_forecast = super(ProphetForecaster, self)._check_ts_forecast(n_forecast)
#
self._prophet_logger.info("Fitting using all data....")
self._mode = 'forecast'
self.ts_fit(suppress=suppress)
self._prophet_logger.info("Forecasting next " + str(n_forecast) + str(self.ts_df.index.freq))
#
future = self._model.make_future_dataframe(periods=n_forecast, freq=self.freq)
if self._weekend_seasonality:
future['on_weekend'] = future['ds'].apply(self.we_season)
future['off_weekend'] = ~future['ds'].apply(self.we_season)
self.forecast = self._model.predict(future)
# confidence intervals
self.lower_conf_int = pd.concat([self.lower_conf_int,
pd.Series(np.asarray(self.forecast['yhat_lower']), index=future.ds)],
axis=0)
self.upper_conf_int = pd.concat([self.upper_conf_int,
pd.Series(np.asarray(self.forecast['yhat_upper']), index=future.ds)],
axis=0)
self.residuals_forecast = None
self.plot_forecast()
def plot_forecast(self):
"""Plot forecasted values"""
if self.residuals_forecast is not None:
fig, axes = super(ProphetForecaster, self)._plot_forecast(y=np.asarray(self._train_dt['y']),
yhat=np.asarray(self.fittedvalues['yhat']),
forecast=pd.Series(
np.asarray(self.forecast['yhat']),
index=self.forecast['ds']), _id='Prophet')
else:
fig_forecast = self._model.plot(self.forecast)
fig_components = self._model.plot_components(self.forecast)
if self._add_change_points:
a = add_changepoints_to_plot(fig_forecast.gca(), self._model, self.forecast)
plt.gcf().autofmt_xdate()
plt.grid(True)
plt.show()
class AutoARIMAForecaster(UVariateTimeSeriesClass):
"""Univariate time series child class using pmdarima.auto_arima for forecasting
Attributes
----------
ref. to https://pypi.org/project/pmdarima/
https://www.alkaline-ml.com/pmdarima/modules/generated/pmdarima.arima.AutoARIMA.html#pmdarima.arima.AutoARIMA
_start_p: int
The starting value for p
_start_q: int
The starting value for q
_test: str
Test for determining the value of d
_max_p: int
The maximal value for p: all values between _start_p and this one will be tried out
_max_q: int
The maximal value for q: all values between _start_q and this one will be tried out
_d: int
The maximum value of d, or the maximum number of non-seasonal differences. If None, this value will be determined.
_seasonal: bool
Seasonal component yes/no
_D:
The order of the seasonal differencing. If None, the value will automatically be selected based on
the results of the seasonal_test.
_start_P: int
The starting value for P
_start_Q: int
The starting value for Q
_max_P: int
The maximum value for P
_max_Q: int
The maximum value for Q
_seasonal_periods (m in original package): int
The period for seasonal differencing, m refers to the number of periods in each season.
For example, m is 4 for quarterly data, 12 for monthly data, or 1 for annual (non-seasonal) data.
Default is 1. Note that if m == 1 (i.e., is non-seasonal),
seasonal will be set to False.
_aarima_trend: str or iterable, default=’c’, ref. http://www.alkaline-ml.com/pmdarima/1.0.0/modules/generated/pmdarima.arima.auto_arima.html
Parameter controlling the deterministic trend polynomial A(t). Can be specified as a string where ‘c’
indicates a constant (i.e. a degree zero component of the trend polynomial),
‘t’ indicates a linear trend with time, and ‘ct’ is both.
Can also be specified as an iterable defining the polynomial as
in numpy.poly1d, where [1,1,0,1] would denote a+bt+ct3.
_random : bool, optional (default=False)
Auto_arima provides the capability to perform a “random search” over a hyper-parameter space.
If random is True, rather than perform an exhaustive search or stepwise search, only n_fits
ARIMA models will be fit (stepwise must be False for this option to do anything).
_n_fits : int, optional (default=10)
If random is True and a “random search” is going to be performed, n_iter is the number of ARIMA models to be fit.
_stepwise : bool, optional (default=True)
Whether to use the stepwise algorithm outlined in Hyndman and Khandakar (2008) to identify the
optimal model parameters.
The stepwise algorithm can be significantly faster than fitting all (or a random subset of)
hyper-parameter combinations and is less likely to over-fit the model.
_information_criterion : str, optional (default=’aic’)
The information criterion used to select the best ARIMA model.
One of pmdarima.arima.auto_arima.VALID_CRITERIA, (‘aic’, ‘bic’, ‘hqic’, ‘oob’).
_scoring : str, optional (default=’mse’)
If performing validation (i.e., if out_of_sample_size > 0), the metric to use for scoring the
out-of-sample data. One of {‘mse’, ‘mae’}
_out_of_sample_size : int, optional (default=0)
The ARIMA class can fit only a portion of the data if specified, in order to retain an “out of bag” sample score.
This is the number of examples from the tail of the time series to hold out and use as validation examples.
The model will not be fit on these samples, but the observations will be added into the model’s endog and exog
arrays so that future forecast values originate from the end of the endogenous vector.
_aarima_logger: Logger
The logger for logging
Methods
----------
assertions()
Assertion tests, must be overrided
set_params()
Sets new parameter values
get_params_dict()
Gets parameter values as a dictionary
ts_fit()
Fits the auto_arima model to time series
ts_diagnose()
Diagnoses the fitted model
plot_residuals()
Generates residual plots
ts_test()
Evaluates fitted model on the test data, if this one has been generated
ts_forecast()
Forecasts time series and plots the results
plot_forecasts()
Plots forecasted time-series
"""
def __init__(self,
start_p=1,
start_q=1,
max_p=3,
max_q=3,
d=None,
D=None,
start_P=1,
start_Q=1,
max_P=3,
max_Q=3,
random=False,
n_fits=10,
stepwise=True,
information_criterion='aic',
scoring='mse',
out_of_sample_size=0,
**kwds):
"""Initializes the object AutoARIMAForecaster"""
self._aarima_logger = Logger("AutoARIMA")
self._aarima_seasonal = False
self._aarima_trend = 'c'
self._start_p = start_p
self._start_q = start_q
self._max_p = max_p
self._max_q = max_q
self._d = d
self._D = D
self._start_P = start_P
self._start_Q = start_Q
self._max_P = max_P
self._max_Q = max_Q
self._random = random
self._n_fits = n_fits
self._stepwise = stepwise
self._information_criterion = information_criterion
self._scoring = scoring
self._out_of_sample_size = out_of_sample_size
try:
super(AutoARIMAForecaster, self).__init__(**kwds)
except TypeError:
self._aarima_logger.exception("Arguments missing...")
AutoARIMAForecaster._init_trend(self)
AutoARIMAForecaster._init_seasonal(self)
AutoARIMAForecaster.assertions(self)
self._id = 'Auto_ARIMA'
def _init_trend(self):
if self._trend is None or self._trend == 'constant':
self._aarima_trend = 'c'
elif self._trend == 'linear':
self._aarima_trend = 't'
elif self._trend == 'constant linear':
self._aarima_trend = 'ct'
elif self._trend in ['additive', 'add']:
# self._aarima_logger.warning("The trend " + str(self._trend) + " not supported by AutoARIMA! "
# "Assuming first order trend")
self._aarima_trend = 'a+bt'
elif self._trend in ['multiplicative', 'mul']:
# self._aarima_logger.warning("The trend " + str(self._trend) + " not supported by AutoARIMA! "
# "Assuming first order trend")
self._aarima_trend = 'a+bt'
def _init_seasonal(self):
if self._seasonal is None:
self._aarima_seasonal = False
if isinstance(self._seasonal, bool):
self._aarima_seasonal = self._seasonal
else:
self._aarima_seasonal = False
def __copy__(self):
"""Copies the object"""
result = super(AutoARIMAForecaster, self).__copy__()
result._start_p = self._start_p
result.start_q = self._start_q
result._test = self._test
result._max_p = self._max_p
result._max_q = self._max_q
result._d = self._d
result._aarima_trend = self._aarima_trend
result._aarima_seasonal = self._aarima_seasonal
result._D = self._D
result._start_P = self._start_P
result._start_Q = self._start_Q
result._max_P = self._max_P
result._max_Q = self._max_Q
result._random = self._random
result._n_fits = self._n_fits
result._stepwise = self._stepwise
result._information_criterion = self._information_criterion
result._scoring = self._scoring
result._out_of_sample_size = self._out_of_sample_size
result._aarima_logger = self._aarima_logger
return result
def assertions(self):
try:
assert self.hyper_params is None
except AssertionError:
self._aarima_logger.exception(
"Hyper parameters does not make sence for Auto ARIMA! "
"Please specify parameters")
sys.exit("STOP")
try:
assert self._aarima_trend is not None
except AssertionError:
self._aarima_logger.exception(
"Assertion Error, trend cannot be None!")
sys.exit("STOP")
try:
assert isinstance(self._aarima_seasonal, bool)
except AssertionError:
self._aarima_logger.exception(
"Assertion Error, seasonal must be boolean True/False")
sys.exit("STOP")
def set_params(self, p_dict=None, **kwargs):
"""Sets new parameter values"""
params_dict = kwargs
if p_dict is not None:
params_dict = p_dict
#
for k, v in params_dict.items():
if k == 'ts_df':
self.ts_df = v
elif k == 'freq':
self.freq = v
elif k == 'n_test':
self.n_test = v
elif k == 'n_val':
self.n_val = v
elif k == 'time_format':
self.time_format = v
elif k == 'start_p':
self._start_p = v
elif k == 'max_p':
self._max_p = v
elif k == 'start_q':
self._start_q = v
elif k == 'max_q':
self._max_q = v
elif k == 'd':
self._d = v
elif k == 'trend':
self._aarima_trend = v
elif k == 'seasonal':
self._aarima_seasonal = v
elif k == 'seasonal_periods':
self._seasonal_periods = v
elif k == 'start_P':
self._start_P = v
elif k == 'max_P':
self._max_P = v
elif k == 'start_Q':
self._start_Q = v
elif k == 'max_Q':
self._max_Q = v
elif k == 'D':
self._D = v
elif k == 'random':
self._random = v
elif k == 'n_fits':
self._n_fits = v
elif k == 'stepwise':
self._stepwise = v
elif k == 'information_criterion':
self._information_criterion = v
elif k == 'scoring':
self._scoring = v
elif k == 'out_of_sample_size':
self._out_of_sample_size = v
self.assertions()
return self
def get_params_dict(self):
"""Gets parameter values as dictionary"""
return {
'start_p': self._start_p,
'start_q': self._start_q,
'test': self._test,
'max_p': self._max_p,
'max_q': self._max_q,
'd': self._d,
'trend': self._aarima_trend,
'seasonal': self._aarima_seasonal,
'seasonal_periods': self._seasonal_periods,
'D': self._D,
'start_P': self._start_P,
'start_Q': self._start_Q,
'max_P': self._max_P,
'max_Q': self._max_Q,
'random': self._random,
'n_fits': self._n_fits,
'stepwise': self._stepwise,
'information_criterion': self._information_criterion,
'scoring': self._scoring,
'out_of_sample_size': self._out_of_sample_size
}
def ts_fit(self, suppress=False):
"""Fit Auto ARIMA to the time series data.
Parameters:
----------
suppress: bool
Suppress or not some of the output messages
"""
self._prepare_fit()
self.ts_split()
self._init_trend()
self._init_seasonal()
ts_df = self._train_dt.copy()
"""
Fit
"""
self._aarima_logger.info("Trying to fit the Auto ARIMA model....")
# tic
start = time()
try:
if not suppress:
self._aarima_logger.info("...via using parameters\n")
print_attributes(self)
self.model_fit = pm.auto_arima(
ts_df,
start_p=self._start_p,
start_q=self._start_q,
test=self._test,
max_p=self._max_p,
m=self._seasonal_periods,
d=self._d,
seasonal=self._aarima_seasonal,
D=self._D,
start_P=self._start_P,
max_P=self._max_P,
trend=self._aarima_trend,
trace=True,
error_action='ignore',
suppress_warnings=True,
stepwise=self._stepwise,
random=self._random,
n_fits=self._n_fits,
scoring=self._scoring,
out_of_sample_size=self._out_of_sample_size,
information_criterion=self._information_criterion)
except (Exception, ValueError):
self._aarima_logger.exception("Exception occurred in the fit...")
self._aarima_logger.warning("Will try to reset some parameters...")
try:
self.model_fit = pm.auto_arima(
ts_df,
start_p=self._start_p,
start_q=self._start_q,
test=self._test,
max_p=self._max_p,
m=1,
d=0,
seasonal=self._aarima_seasonal,
D=0,
start_P=self._start_P,
max_P=self._max_P,
trend=self._aarima_trend,
trace=True,
error_action='ignore',
suppress_warnings=True,
stepwise=self._stepwise,
random=self._random,
n_fits=self._n_fits,
scoring=self._scoring,
out_of_sample_size=self._out_of_sample_size,
information_criterion=self._information_criterion)
except (Exception, ValueError):
self._aarima_logger.exception("Exception occurred")
self._aarima_logger.error("Please try other parameters!")
self.model_fit = None
else:
# toc
self._aarima_logger.info("Time elapsed: {} sec.".format(time() -
start))
#
self._aarima_logger.info("Model successfully fitted to the data!")
self._aarima_logger.info("The chosen model AIC: " +
str(self.model_fit.aic()))
# Fitted values
self._aarima_logger.info(
"Computing fitted values and residuals...")
self.fittedvalues = pd.Series(self.model_fit.predict_in_sample(
start=0, end=(len(ts_df) - 1)),
index=ts_df.index)
# Residuals
super(AutoARIMAForecaster, self)._residuals()
self._aarima_logger.info("Done.")
return self
def ts_diagnose(self):
"""Diagnose the model"""
try:
assert self.model_fit is not None
except AssertionError:
self._aarima_logger.exception(
"Model has to be fitted first! Please call ts_fit(...)")
sys.exit("STOP")
self.model_fit.plot_diagnostics(figsize=(9, 3.5))
self.plot_residuals()
def plot_residuals(self):
"""Plot the residuals"""
fig, axis = super(AutoARIMAForecaster, self)._plot_residuals(
y=np.asarray(self._train_dt['y']),
yhat=np.asarray(self.fittedvalues),
_id=" Auto ARIMA")
plt.gcf().autofmt_xdate()
plt.grid(True)
plt.show()
def ts_test(self, show_plot=True):
"""Test the fitted model if test data available"""
if super(AutoARIMAForecaster, self)._check_ts_test() < 0:
return
n_forecast = len(self._test_dt)
self._aarima_logger.info(
"Evaluating the fitted ARIMA model on the test data...")
future, confint = self.model_fit.predict(n_periods=n_forecast,
return_conf_int=True)
self.forecast = pd.Series(future, index=self._test_dt.index)
self.lower_conf_int = pd.Series(confint[:, 0],
index=self._test_dt.index)
self.upper_conf_int = pd.Series(confint[:, 1],
index=self._test_dt.index)
self.residuals_forecast = pd.Series(np.asarray(self._test_dt['y']) -
np.asarray(self.forecast),
index=self._test_dt.index)
self.measure_rmse()
self._aarima_logger.info("RMSE on test data: {}".format(self.rmse))
# plot
if show_plot:
self.plot_forecast()
def ts_forecast(self, n_forecast, suppress=False):
"""Forecast time series over time frame in the future specified via n_forecast"""
#
n_forecast = super(AutoARIMAForecaster,
self)._check_ts_forecast(n_forecast)
#
self._aarima_logger.info("Fitting using all data....")
self._mode = 'forecast'
self.ts_fit(suppress=suppress)
self._aarima_logger.info("Forecasting next " + str(n_forecast) +
str(self.ts_df.index.freq))
#
future, confint = self.model_fit.predict(n_periods=n_forecast,
return_conf_int=True)
idx_future = self._gen_idx_future(n_forecast=n_forecast)
self.forecast = pd.Series(future, index=idx_future)
if self.lower_conf_int is None and self.upper_conf_int is None:
self.lower_conf_int = pd.Series(confint[:, 0], index=idx_future)
self.upper_conf_int = pd.Series(confint[:, 1], index=idx_future)
else:
self.lower_conf_int = pd.concat([
self.lower_conf_int,
pd.Series(confint[:, 0], index=idx_future)
],
axis=0)
self.upper_conf_int = pd.concat([
self.upper_conf_int,
pd.Series(confint[:, 1], index=idx_future)
],
axis=0)
self.residuals_forecast = None
# self.plot_forecast()
return self
def plot_forecast(self):
"""Plot forecasted values"""
fig, axis = super(AutoARIMAForecaster, self)._plot_forecast(
y=np.asarray(self._train_dt['y']),
yhat=np.asarray(self.fittedvalues),
forecast=self.forecast,
_id='Auto ARIMA')
plt.gcf().autofmt_xdate()
plt.grid(True)
plt.show()
class UVariateTimeSeriesClass(object):
"""
Uni-variate time series class
Attributes:
_ts_df_cols - internal column names for dataframe that will be input to model
ts_df - time series data frame
freq - frequency of time series, possibilities ['S', 'min', 'H', 'D', 'W', 'M']
p_train - float value defining which part of data is to be used as training data. Note, value of 1.0 would mean
all data will be used as training data,
hence no test data will be generated.
timeformat - time format if time series data needs to be brought into datetime
#
_mode - defines the mode as 'test' or 'forecast'
_train_dt - training data
_test_dt - test data
model_fit - fitted model
fittedvalues - computed fitted values
residuals - residuals
rmse - RMSE on test set (test data and the forecast on test data)
upper_whisker_res - upper whisker for residuals
lower_conf_int - upper confidence interval
upper_conf_int - lower confidence interval
forecast - computed forcatsed values
residuals_forecast - residuals between forecasted and real values. Note, this variable exist only if test data
existed
Methods:
ts_transform() - transforms time series using log10 or box-cox
ts_resample() - resamples time series at the chosen frequency freq
_plot_residuals() - residual plots helper function
ts_test() - evaluates fitted model on the test data, if this one has been generated
ts_forecast() - forecasts time series and plots the results
_plot_forecast() - helper function for plotting forecasted time-series
ts_decompose() - decomposes time series in seasonal, trend and resduals and plots the results
plot_decompose() - plots the results of ts_decompose()
Helper methods:
_prepare_fit() - prepares ts_fit of child class. Supposed to be called by a child class
_residuals() - helper function for calculating residuals. Supposed to be called by a child class
_check_ts_test() - checks for test. Supposed to be called by a child class
_check_ts_forecast() - checks for forecast. Supposed to be called by a child class
"""
def __init__(self, ts_df, time_format="%Y-%m-%d %H:%M:%S", freq='D', p_train=1.0, **kwds):
"""
Initializes the object UVariateTimeSeriesForecaster
"""
self._ts_df_cols = ['ds', 'y']
self.ts_df = ts_df
self.time_format = time_format
self.freq = freq
self.p_train = p_train
self.transform = None
self._boxcox_lmbda = None
self._mode = ''
self._train_dt = None
self._test_dt = None
self.model_fit = None
self.fittedvalues = None
self.residuals = None
self.rmse = None
self.upper_whisker_res = None
self.lower_conf_int = None
self.upper_conf_int = None
self.forecast = None
self.residuals_forecast = None
self.seasonal = None
self.trend = None
self.baseline = None
self._uvts_cls_logger = Logger('uvts_cls')
# Assertion Tests
try:
assert self.freq in ['S', 'min', 'H', 'D', 'W', 'M']
except AssertionError:
self._uvts_cls_logger.warning("freq should be in ['S', 'min', 'H', 'D', W', 'M']. "
"Assuming daily frequency!")
self.freq = 'D'
try:
self.p_train = float(self.p_train)
assert self.p_train > 0
except AssertionError:
self._uvts_cls_logger.error("p_train defines part of data on which you would train your model."
"This value cannot be less than or equal to zero!")
self._uvts_cls_logger.exception("Exception occurred, p_train")
except ValueError:
self._uvts_cls_logger.error("p_train must be convertible to float type!")
self._uvts_cls_logger.exception("Exception occurred, p_train")
else:
if int(self.p_train) < 1:
self._mode = 'test'
else:
self._mode = 'forecast'
try:
assert pd.DataFrame(self.ts_df).shape[1] <= 2
except AssertionError:
self._uvts_cls_logger.error(
"Time series must be uni-variate. "
"Hence, at most a time columns and a column of numeric values are expected!")
self._uvts_cls_logger.exception("Exception occurred, ts_df")
else:
self.ts_df = self.ts_df.reset_index()
self.ts_df.columns = self._ts_df_cols
self.ts_df['y'] = self.ts_df['y'].apply(np.float64, errors='coerce')
self.ts_df.set_index('ds', inplace=True)
print(type(self._uvts_cls_logger))
print(self._uvts_cls_logger)
self._uvts_cls_logger.info("Using time series data of range: " + str(min(self.ts_df.index)) + ' - ' + str(
max(self.ts_df.index)) + " and shape: " + str(self.ts_df.shape))
if not isinstance(self.ts_df.index, pd.DatetimeIndex):
self._uvts_cls_logger.warning("Time conversion required...")
self.ts_df = self.ts_df.reset_index()
try:
self.ts_df['ds'] = self.ts_df['ds'].apply(
lambda x: datetime.datetime.strptime(
str(x).translate({ord('T'): ' ', ord('Z'): None})[:-1],
self.time_format))
except ValueError as e:
self._uvts_cls_logger.warning("Zulu time conversion not successful: {}".format(e))
self._uvts_cls_logger.warning("Will try without assuming zulu time...")
try:
self.ts_df['ds'] = self.ts_df['ds'].apply(
lambda x: datetime.datetime.strptime(str(x), self.time_format))
except ValueError as e:
self._uvts_cls_logger.info("Time conversion not successful. Check your time_format: {}".format(e))
else:
self._uvts_cls_logger.info("Time conversion successful!")
else:
self._uvts_cls_logger.info("Time conversion successful!")
# set index
self.ts_df.set_index('ds', inplace=True)
#
self.ts_df.index = pd.to_datetime(self.ts_df.index)
self.ts_df.sort_index(inplace=True)
# resample
self.ts_resample()
# delegate
super(UVariateTimeSeriesClass, self).__init__(**kwds)
def __copy__(self):
"""
Copies the object
"""
cls = self.__class__
result = cls.__new__(cls)
result.__dict__.update(self.__dict__)
return result
def __deepcopy__(self, memo):
"""
Deepcopies the object
"""
cls = self.__class__
result = cls.__new__(cls)
memo[id(self)] = result
for k, v in self.__dict__.items():
setattr(result, k, deepcopy(v, memo))
return result
def ts_transform(self, transform):
"""
Transforms time series via applying casted 'transform'. Right now 'log10' and 'box-cox' possible.
"""
try:
assert transform.lower().strip() in ['log10', 'box-cox']
except AssertionError:
self._uvts_cls_logger.error(
"transform should be in ['log10', 'box-cox'] or empty. Assuming no transform! "
"Hence, if you get bad results, you would like maybe to choose e.g., log10 here.")
self._uvts_cls_logger.exception("Assertion exception occurred, transform")
self.transform = None
else:
self.transform = transform.lower()
# transform
if self.transform == 'log10':
try:
self.ts_df['y'] = self.ts_df['y'].apply(np.log10)
except ValueError:
self._uvts_cls_logger.exception("log10 transformation did not work! Possibly negative "
"values present?")
elif self.transform == 'box-cox':
if input("Do you want to provide lambda for box.cox? y/n?").strip().lower() == 'y':
self._boxcox_lmbda = float(input())
else:
self._boxcox_lmbda = None
try:
if self._boxcox_lmbda is None:
bc, lmbda_1 = stats.boxcox(self.ts_df['y'], lmbda=self._boxcox_lmbda)
self.ts_df['y'] = stats.boxcox(self.ts_df['y'], lmbda=lmbda_1)
else:
self.ts_df['y'] = stats.boxcox(self.ts_df['y'], lmbda=self._boxcox_lmbda)
except ValueError:
self._uvts_cls_logger.exception("box-cox transformation did not work! "
"Possibly negative values present or bad lmbda?")
return self
def set_frequency(self, new_freq):
"""
Sets new frequency and resamples time series to that new frequency
"""
try:
assert new_freq in ['S', 'min', 'H', 'D', 'W', 'M']
except AssertionError:
self._uvts_cls_logger.error("frequency should be in ['S', 'min', 'H', 'D', W', 'M']")
else:
self.freq = new_freq
self.ts_resample()
def ts_check_frequency(self):
"""
Checks the frequency of time series
"""
if self.ts_df.index.freq is None:
self._uvts_cls_logger.info("No specific frequency detected.")
self._uvts_cls_logger.info("Frequency chosen in initialization: " + str(
self.freq) + " enter 'n' and call ts_resample() if you are satisfied with this value.")
if input("Should a histogram of time deltas be plotted y/n?").strip().lower() == 'y':
ff = pd.Series(self.ts_df.index[1:(len(self.ts_df))] - self.ts_df.index[0:(len(self.ts_df) - 1)])
ff = ff.apply(lambda x: int(x.total_seconds() / (60 * 60)))
plt.hist(ff, bins=120)
plt.xlabel("Rounded time delta [H]")
plt.ylabel("Frequency of occurrence")
self._uvts_cls_logger.info(ff.value_counts())
self._uvts_cls_logger.info("Should hourly frequency not fit, choose a reasonable frequency and call "
"set_frequency(new_freq)")
else:
pass
else:
self._uvts_cls_logger.info("Time series frequency: " + str(self.ts_df.index.freq))
def ts_resample(self):
"""
Brings original time series to the chosen frequency freq
"""
ts_freq = pd.DataFrame(
index=pd.date_range(self.ts_df.index[0], self.ts_df.index[len(self.ts_df) - 1], freq=self.freq),
columns=['dummy'])
self.ts_df = ts_freq.join(self.ts_df).drop(['dummy'], axis=1)
self.ts_df.y = self.ts_df.y.fillna(method='ffill')
# if np.isnan ( self.ts_df.y ).any ():
# self.ts_df.y = self.ts_df.y.fillna ( method='bfill' )
if np.isnan(self.ts_df.y).any():
self._uvts_cls_logger.warning("Some NaN found, something went wrong, check the data!")
sys.exit(-1)
self._uvts_cls_logger.info("Time series resampled at frequency: " + str(self.ts_df.index.freq) +
". New shape of the data: " + str(self.ts_df.shape))
return self
def _prepare_fit(self):
"""
Prepares data for training or forecasting modes
"""
if self.ts_df.index.freq is None:
self._uvts_cls_logger.warning("Time series exhibit no frequency. Calling ts_resample()...")
try:
self.ts_resample()
except ValueError:
self._uvts_cls_logger.error("Resample did not work! Error:" + str(sys.exc_info()[0]))
sys.exit("STOP")
ts_df = self.ts_df
ts_test_df = pd.DataFrame()
if self._mode == 'forecast' or int(self.p_train) == 1:
self._train_dt = ts_df
self._test_dt = ts_test_df
elif self._mode == 'test' and int(self.p_train) < 1:
# split
ts_df = ts_df.reset_index()
ts_df.columns = self._ts_df_cols
ts_test_df = ts_df
# training
ts_df = pd.DataFrame(ts_df.loc[:int(self.p_train * len(ts_df) - 1), ])
ts_df.set_index('ds', inplace=True)
# test
ts_test_df = pd.DataFrame(ts_test_df.loc[int(self.p_train * len(ts_test_df)):, ])
ts_test_df.set_index('ds', inplace=True)
# now set
self._train_dt = ts_df
if not ts_test_df.empty:
self._test_dt = ts_test_df
return self
def _residuals(self):
"""
Calculate residuals
"""
if self.model_fit is None:
self._uvts_cls_logger.error("No model has been fitted, residuals cannot be computed!")
sys.exit("STOP")
try:
# use fittedvalues to fill in the model dictionary
self.residuals = pd.Series(np.asarray(self._train_dt['y']) - np.asarray(self.fittedvalues).flatten(),
index=self._train_dt['y'].index)
self.upper_whisker_res = self.residuals.mean() + 1.5 * (
self.residuals.quantile(0.75) - self.residuals.quantile(0.25))
except (KeyError, AttributeError):
self._uvts_cls_logger.exception("Exception occurred: Model was not fitted or ts has other structure")
return self
def _plot_residuals(self, y, yhat, _id):
"""
Plot the residuals
"""
try:
assert self.model_fit is not None
except AssertionError:
self._uvts_cls_logger.exception("Model has to be fitted first! Please call ts_fit(...)")
fig, axes = plt.subplots(2, 1, figsize=(20, 5), sharex=True)
axes[0].plot(pd.Series(yhat, index=self._train_dt.index), color='y', linewidth=2.0)
axes[0].plot(pd.Series(y, index=self._train_dt.index), color='b')
axes[0].set_ylabel("Model Fit")
axes[0].set_title("Real (blue) and estimated values, " + str(_id))
#
axes[1].plot(self.residuals, color="r")
if self.forecast is not None and self.residuals_forecast is None \
and self.lower_conf_int is not None and self.upper_conf_int is not None:
axes[0].fill_between(self.lower_conf_int.index, self.lower_conf_int, self.upper_conf_int, color='k',
alpha=.15)
if self.upper_whisker_res is not None:
axes[1].axhline(y=self.upper_whisker_res, xmin=0, xmax=1, color='m', label='upper_whisker', linestyle='--',
linewidth=1.5)
axes[1].axhline(y=-self.upper_whisker_res, xmin=0, xmax=1, color='m', label='upper_whisker', linestyle='--',
linewidth=1.5)
axes[1].set_ylabel('Residuals')
axes[1].set_title('Difference between model output and the real data and +/- upper whisker, ' + str(_id))
return fig, axes
def _check_ts_test(self):
"""
Check before ts_test is child class is called
"""
try:
assert self.model_fit is not None
except AssertionError:
self._uvts_cls_logger.exception("Model has to be fitted first! Please call ts_fit(...)")
try:
assert self._test_dt is not None
except(KeyError, AssertionError):
self._uvts_cls_logger.exception("Nothing to validate. "
"Call ts_forecast() or specify amount of training data "
"when initializing the object.")
return -1
else:
self._mode = 'test'
return 0
def _check_ts_forecast(self, n_forecast):
"""
Check before ts_forecast in child class is called
"""
#
try:
n_forecast = int(n_forecast)
assert 0 < n_forecast < len(self._train_dt)
except AssertionError:
self._uvts_cls_logger.exception("Number of periods to be forecasted is too low, too high or not numeric!")
except ValueError:
self._uvts_cls_logger.exception("n_forecast must be convertible to int type!")
try:
assert self.model_fit is not None
except AssertionError:
self._uvts_cls_logger.exception("Model has to be fitted first! Please call ts_fit(...)")
return n_forecast
def _gen_idx_future(self, n_forecast):
idx_future = None
if self.freq == 'S':
idx_future = pd.date_range(start=max(self._train_dt.index),
end=max(self._train_dt.index) + datetime.timedelta(
seconds=n_forecast - 1), freq='S')
elif self.freq == 'min':
idx_future = pd.date_range(start=max(self._train_dt.index),
end=max(self._train_dt.index) + datetime.timedelta(
minutes=n_forecast - 1), freq='min')
elif self.freq == 'H':
idx_future = pd.date_range(start=max(self._train_dt.index),
end=max(self._train_dt.index) + datetime.timedelta(
hours=n_forecast - 1), freq='H')
elif self.freq == 'D':
idx_future = pd.date_range(start=max(self._train_dt.index),
end=max(self._train_dt.index) + datetime.timedelta(
days=n_forecast - 1), freq='D')
elif self.freq == 'W':
idx_future = pd.date_range(start=max(self._train_dt.index),
end=max(self._train_dt.index) + datetime.timedelta(
weeks=n_forecast - 1), freq='W')
elif self.freq == 'M':
idx_future = pd.date_range(start=max(self._train_dt.index),
end=max(self._train_dt.index) + relativedelta(months=+(n_forecast - 1)),
freq='M')
return idx_future
def _plot_forecast(self, y, yhat, forecast, _id):
"""
Plot forecasted values
"""
try:
assert self.model_fit is not None
except AssertionError:
self._uvts_cls_logger.exception("Model has to be fitted first! Please call ts_fit(...)")
sys.exit("STOP")
#
try:
assert self.forecast is not None
except AssertionError:
self._uvts_cls_logger.exception("Neither ts_test(...) nor ts_forecast(...) have been called yet!")
sys.exit("STOP")
fig, axes = plt.subplots(2, 1, figsize=(20, 7), sharex=True)
#
axes[0].plot(pd.Series(yhat, index=self._train_dt.index), color='y', linewidth=2.0)
axes[0].plot(pd.Series(y, index=self._train_dt.index), color='b')
#
if self.residuals_forecast is not None:
axes[0].plot(self.ts_df, color='b')
axes[0].plot(forecast, color='darkgreen')
#
if self.lower_conf_int is not None and self.upper_conf_int is not None:
axes[0].fill_between(self.lower_conf_int.index,
self.lower_conf_int,
self.upper_conf_int,
color='k', alpha=.15)
axes[0].set_ylabel("Fit and Forecast/Validation")
axes[0].set_title("Real (blue), estimated (yellow) and forecasted values, " + str(_id))
#
if self.residuals_forecast is not None:
axes[1].plot(pd.concat([self.residuals, self.residuals_forecast], axis=0), color='r')
axes[1].plot(self.residuals, color="r")
if self.upper_whisker_res is not None:
axes[1].axhline(y=self.upper_whisker_res,
xmin=0,
xmax=1, color='m',
label='upper_whisker',
linestyle='--', linewidth=1.5)
axes[1].axhline(y=-self.upper_whisker_res,
xmin=0,
xmax=1, color='m',
label='upper_whisker',
linestyle='--', linewidth=1.5)
axes[1].set_ylabel("Residuals")
axes[1].set_title("Difference between model output and the real data both, for fitted "
"and forecasted and +/- upper whisker or confidence intervals, " + str(_id))
return fig, axes
def ts_decompose(self, params=None):
"""
Decomposes time series into trend, seasonal and residual
"""
if params is None:
params = dict({'model': 'additive',
'freq': 1})
try:
assert isinstance(params, dict)
except AssertionError:
self._uvts_cls_logger.exception("Dictionary is expected for parameters!")
sys.exit("STOP")
try:
assert 'model' in list(params.keys())
except AssertionError:
self._uvts_cls_logger.exception("Unexpected dictionary keys. At least decomposition "
"model must be supplied!")
sys.exit("STOP")
if 'freq' not in list(params.keys()):
params['freq'] = 1
try:
if self.ts_df.index.freq is not None:
res = seasonal_decompose(self.ts_df.loc[:, 'y'], model=params['model'])
else:
res = seasonal_decompose(self.ts_df.loc[:, 'y'], model=params['model'], freq=params['freq'])
except ValueError:
self._uvts_cls_logger.exception("ValueError, seasonal_decompose error")
else:
self.seasonal = res.seasonal
self.trend = res.trend
self.baseline = self.seasonal + self.trend
self.residuals = res.resid
self.upper_whisker_res = self.residuals.mean() + 1.5 * (
self.residuals.quantile(0.75) - self.residuals.quantile(0.25))
def plot_decompose(self):
try:
assert self.seasonal is not None
except AssertionError:
self.ts_decompose()
fig, axes = plt.subplots(4, 1, figsize=(20, 7), sharex=True)
axes[0].plot(self.trend)
axes[0].set_title("Trend")
#
axes[1].plot(self.seasonal)
axes[1].set_title("Seasonality")
#
axes[2].plot(self.baseline)
axes[2].set_title("Baseline")
#
axes[3].plot(self.residuals)
axes[3].set_title("Residuals")
#
if self.upper_whisker_res is not None:
axes[3].axhline(y=self.upper_whisker_res,
xmin=0,
xmax=1, color='m',
label='upper_whisker',
linestyle='--', linewidth=1.5)
axes[3].axhline(y=-self.upper_whisker_res,
xmin=0,
xmax=1, color='m',
label='upper_whisker',
linestyle='--', linewidth=1.5)
plt.gcf().autofmt_xdate()
plt.grid(True)
plt.show()
def ts_fit(self):
# stop the delegation chain
assert not hasattr(super(), 'ts_fit')
# root mean squared error or rmse
def measure_rmse(self):
try:
assert self.residuals_forecast is not None
except AssertionError:
self._uvts_cls_logger.exception("AssertionError occurred, Cannot compute RMSE! Check your object mode")
self.rmse = np.sqrt(np.square(self.residuals_forecast).mean())
def __init__(self, ts_df, time_format="%Y-%m-%d %H:%M:%S", freq='D', p_train=1.0, **kwds):
"""
Initializes the object UVariateTimeSeriesForecaster
"""
self._ts_df_cols = ['ds', 'y']
self.ts_df = ts_df
self.time_format = time_format
self.freq = freq
self.p_train = p_train
self.transform = None
self._boxcox_lmbda = None
self._mode = ''
self._train_dt = None
self._test_dt = None
self.model_fit = None
self.fittedvalues = None
self.residuals = None
self.rmse = None
self.upper_whisker_res = None
self.lower_conf_int = None
self.upper_conf_int = None
self.forecast = None
self.residuals_forecast = None
self.seasonal = None
self.trend = None
self.baseline = None
self._uvts_cls_logger = Logger('uvts_cls')
# Assertion Tests
try:
assert self.freq in ['S', 'min', 'H', 'D', 'W', 'M']
except AssertionError:
self._uvts_cls_logger.warning("freq should be in ['S', 'min', 'H', 'D', W', 'M']. "
"Assuming daily frequency!")
self.freq = 'D'
try:
self.p_train = float(self.p_train)
assert self.p_train > 0
except AssertionError:
self._uvts_cls_logger.error("p_train defines part of data on which you would train your model."
"This value cannot be less than or equal to zero!")
self._uvts_cls_logger.exception("Exception occurred, p_train")
except ValueError:
self._uvts_cls_logger.error("p_train must be convertible to float type!")
self._uvts_cls_logger.exception("Exception occurred, p_train")
else:
if int(self.p_train) < 1:
self._mode = 'test'
else:
self._mode = 'forecast'
try:
assert pd.DataFrame(self.ts_df).shape[1] <= 2
except AssertionError:
self._uvts_cls_logger.error(
"Time series must be uni-variate. "
"Hence, at most a time columns and a column of numeric values are expected!")
self._uvts_cls_logger.exception("Exception occurred, ts_df")
else:
self.ts_df = self.ts_df.reset_index()
self.ts_df.columns = self._ts_df_cols
self.ts_df['y'] = self.ts_df['y'].apply(np.float64, errors='coerce')
self.ts_df.set_index('ds', inplace=True)
print(type(self._uvts_cls_logger))
print(self._uvts_cls_logger)
self._uvts_cls_logger.info("Using time series data of range: " + str(min(self.ts_df.index)) + ' - ' + str(
max(self.ts_df.index)) + " and shape: " + str(self.ts_df.shape))
if not isinstance(self.ts_df.index, pd.DatetimeIndex):
self._uvts_cls_logger.warning("Time conversion required...")
self.ts_df = self.ts_df.reset_index()
try:
self.ts_df['ds'] = self.ts_df['ds'].apply(
lambda x: datetime.datetime.strptime(
str(x).translate({ord('T'): ' ', ord('Z'): None})[:-1],
self.time_format))
except ValueError as e:
self._uvts_cls_logger.warning("Zulu time conversion not successful: {}".format(e))
self._uvts_cls_logger.warning("Will try without assuming zulu time...")
try:
self.ts_df['ds'] = self.ts_df['ds'].apply(
lambda x: datetime.datetime.strptime(str(x), self.time_format))
except ValueError as e:
self._uvts_cls_logger.info("Time conversion not successful. Check your time_format: {}".format(e))
else:
self._uvts_cls_logger.info("Time conversion successful!")
else:
self._uvts_cls_logger.info("Time conversion successful!")
# set index
self.ts_df.set_index('ds', inplace=True)
#
self.ts_df.index = pd.to_datetime(self.ts_df.index)
self.ts_df.sort_index(inplace=True)
# resample
self.ts_resample()
# delegate
super(UVariateTimeSeriesClass, self).__init__(**kwds)
def __init__(self,
dict_hyper_params,
ensemble=['dlm', 'prophet'],
show_plots=True,
**kwds):
"""Initialized the object EnsembleForecaster"""
self._model_list = [
'arima', 'sarima', 'exponential smoothing', 'prophet', 'dlm',
'linear'
]
self.ensemble = list(map(lambda x: x.lower(), ensemble))
self.dict_hyper_params = dict_hyper_params
self.show_plots = show_plots
self._dict_models = dict() # dict.fromkeys(self.ensemble, None)
self._best_models = dict()
self.best_ensemble = dict()
self._ensemble_logger = Logger("ensemble")
try:
super(EnsembleForecaster, self).__init__(**kwds)
except (TypeError, AttributeError) as e:
self._ensemble_logger.exception("Arguments missing...{}".format(e))
self._id = 'Ensemble'
#
if 'prophet' in self.ensemble:
self._dict_models['prophet'] = self.__copy__()
self._dict_models['prophet'].__class__ = ProphetForecaster
if 'linear' in self.ensemble:
self._dict_models['linear'] = self.__copy__()
self._dict_models['linear'].__class__ = LinearForecaster
if 'arima' in self.ensemble:
self._dict_models['arima'] = self.__copy__()
self._dict_models['arima'].__class__ = ARIMAForecaster
if 'sarima' in self.ensemble:
self._dict_models['sarima'] = self.__copy__()
self._dict_models['sarima'].__class__ = SARIMAForecaster
if 'exponential smoothing' in self.ensemble:
self._dict_models['expsm'] = self.__copy__()
self._dict_models[
'expsm'].__class__ = ExponentialSmoothingForecaster
if 'dlm' in self.ensemble:
self._dict_models['dlm'] = self.__copy__()
self._dict_models['dlm'].__class__ = DLMForecaster
if 'auto_arima' in self.ensemble:
self._dict_models['auto_arima'] = self.__copy__()
self._dict_models['auto_arima'].__class__ = AutoARIMAForecaster
if 'all' in self.ensemble:
self._dict_models['prophet'] = self.__copy__()
self._dict_models['prophet'].__class__ = ProphetForecaster
self._dict_models['linear'] = self.__copy__()
self._dict_models['linear'].__class__ = LinearForecaster
self._dict_models['arima'] = self.__copy__()
self._dict_models['arima'].__class__ = ARIMAForecaster
self._dict_models['sarima'] = self.__copy__()
self._dict_models['sarima'].__class__ = SARIMAForecaster
self._dict_models['expsm'] = self.__copy__()
self._dict_models[
'expsm'].__class__ = ExponentialSmoothingForecaster
self._dict_models['dlm'] = self.__copy__()
self._dict_models['dlm'].__class__ = DLMForecaster
self._dict_models['auto_arima'] = self.__copy__()
self._dict_models['auto_arima'].__class__ = AutoARIMAForecaster
self.assertions()
class GridSearchClass(object):
"""Class to perform the grid search given the hyper parameters
Attributes
----------
forecaster: Object (tsa)
Forecaster object from the tsa package
hyper_params: dictionary
A dictionary of hyper parameters
results: dictionary
A dictionary where results are saved
best_model: dictionary
A dictionary where the best model and respective hyper parameters are saved
_gs_logger: Logger
The logger for logging
Methods
----------
assertions()
Assertion tests
set_forecaster()
Sets new forecaster
set_hyper_params()
Sets new hyper parameters
grid_search()
Performs grid search trough all combinations of parameters.
Parameter combinations are generated using hyper parameters
"""
def __init__(self, **kwargs):
"""Initializes GridSearch class"""
self._gs_logger = Logger("grid_search")
self.forecaster = None
self.hyper_params = None
for k, v in kwargs.items():
if k == 'forecaster':
self.forecaster = v
elif k == 'hyper_params':
self.hyper_params = v
self.assertions()
self.results = list()
self.best_model = dict()
#
# self._gs_logger.info("Grid Search initialized. Call grid_search()")
def assertions(self):
if self.forecaster is not None:
try:
assert (isinstance(self.forecaster, ProphetForecaster) or isinstance(self.forecaster, DLMForecaster) \
or isinstance(self.forecaster, LinearForecaster) or \
isinstance(self.forecaster, ExponentialSmoothingForecaster) or \
isinstance(self.forecaster, ARIMAForecaster) or isinstance(self.forecaster, SARIMAForecaster)) \
and not isinstance(self.forecaster, UVariateTimeSeriesForecaster)
except AssertionError:
self._gs_logger.exception("Unexpected type for forecaster!")
sys.exit("STOP")
if self.hyper_params is not None:
try:
assert isinstance(self.hyper_params, dict)
except AssertionError:
self._gs_logger.exception("Unexpected type for hyper_params")
sys.exit("STOP")
if hasattr(self.forecaster, 'n_test'):
try:
assert self.forecaster.n_test > 0
except AssertionError:
self._gs_logger.exception("No test data specified for this forecaster. Grid search will stop!")
sys.exit("STOP")
else:
self.forecaster._mode = 'test'
def set_forecaster(self, forecaster_obj):
"""Sets the forecaster"""
self.forecaster = forecaster_obj
self.assertions()
return self
def set_hyper_params(self, hyper_params):
"""Sets hyper parameters"""
self.hyper_params = hyper_params
self.assertions()
return self
@staticmethod
def _print_dict(d):
d_info = ""
for k,v in d.items():
d_info = d_info + "....................... | grid_search | INFO : " + str(k) + " : " + str(v) + "\n"
return "Hyper parameter set: \n" + d_info
def grid_search(self, suppress=False, show_plot=True):
"""Performs the grid search
Via generating all possible combinations of parameters. The combinations are derived from the hyper parameters.
This method assumes that attributes of a forecaster start with '_'
The best model is chosen using rmse computed on the test data as
the measure for the goodness of the forecaster
"""
# set-up parameter sets
for p, v in self.hyper_params.items():
if not isinstance(v, list):
self.hyper_params[p] = [v]
combinations = list(itertools.product(*list(self.hyper_params.values())))
params = [dict(zip(list(self.hyper_params.keys()), combinations[i])) for i in range(len(combinations))]
self._gs_logger.info("{} number of parameter combinations generated".format(len(params)))
#if input("Run grid search y/n?").strip().lower() == 'y':
# reset
self.results = list()
self.best_model = dict()
rmse = np.float('Inf')
for i in range(len(params)):
self._gs_logger.info(self._print_dict(params[i]))
for p, val in params[i].items():
# check
attr = '_'+str(p)
if attr in list(self.forecaster.__dict__.keys()):
_type = type(getattr(self.forecaster, attr))
try:
assert type(val) == _type
except AssertionError:
try:
if str(_type) == 'float':
val = np.float(val)
elif str(_type) == 'int':
val = np.int(val)
elif str(_type) == 'bool':
val = np.bool(val)
elif str(_type) == 'str':
val = str(val)
elif str(_type) == 'NoneType':
pass
self._gs_logger.info("Parameter type mismatch found, however, conversion successful")
except ValueError:
self._gs_logger.exception("Parameter type mismatch: Conversion did not work, "
"please check your hyper parameters!")
raise
setattr(self.forecaster, attr, val)
else:
self._gs_logger.warning("Attribute {} not found. Default value will be used only.".format(attr))
pass
# call ts_fit() and ts_test()
# tic
start = time()
self.forecaster.ts_fit(suppress=suppress)
self.forecaster.ts_test(show_plot=show_plot)
# toc
time_elapsed = time() - start
#
current_results = dict()
current_results['params'] = self.forecaster.get_params_dict()
current_results['rmse'] = self.forecaster.rmse
current_results['time_elapsed'] = time_elapsed
self.results.append(current_results)
#
if self.results[i]['rmse'] < rmse:
rmse = self.results[i]['rmse']
self.best_model['forecaster'] = self.forecaster.__copy__()
self.best_model['hyper_params'] = self.results[i]['params']
self.best_model['rmse'] = self.results[i]['rmse']
self.best_model['time_elapsed'] = self.results[i]['time_elapsed']
self._gs_logger.info("Best parameter combination:")
self._gs_logger.info(self._print_dict(self.best_model['hyper_params']))
self._gs_logger.info("RMSE {} :".format(self.best_model['rmse']))
# else:
# self._gs_logger.info("OK")
return self
