def __init__(
self,
name: str = "TSFreshRegressor",
frequency: str = 'infer',
prediction_interval: float = 0.9,
regression_type: str = None,
holiday_country: str = 'US',
verbose: int = 0,
random_seed: int = 2020,
regression_model: str = 'Adaboost',
max_timeshift: int = 10,
feature_selection: str = None,
):
ModelObject.__init__(
self,
name,
frequency,
prediction_interval,
regression_type=regression_type,
holiday_country=holiday_country,
random_seed=random_seed,
verbose=verbose,
)
self.regression_model = regression_model
self.max_timeshift = max_timeshift
self.feature_selection = feature_selection
def __init__(
self,
name: str = "TensorflowSTS",
frequency: str = 'infer',
prediction_interval: float = 0.9,
regression_type: str = None,
holiday_country: str = 'US',
random_seed: int = 2020,
verbose: int = 0,
trend: str = 'local',
seasonal_periods: int = None,
ar_order: int = None,
fit_method: str = 'hmc',
num_steps: int = 200,
):
ModelObject.__init__(
self,
name,
frequency,
prediction_interval,
regression_type=regression_type,
holiday_country=holiday_country,
random_seed=random_seed,
verbose=verbose,
)
self.seasonal_periods = seasonal_periods
self.ar_order = ar_order
self.trend = trend
self.fit_method = fit_method
self.num_steps = num_steps
def __init__(
self,
name: str = "TFPRegression",
frequency: str = 'infer',
prediction_interval: float = 0.9,
holiday_country: str = 'US',
random_seed: int = 2020,
verbose: int = 1,
kernel_initializer: str = 'lecun_uniform',
optimizer: str = 'adam',
loss: str = 'negloglike',
epochs: int = 50,
batch_size: int = 32,
dist: str = 'normal',
regression_type: str = None,
):
ModelObject.__init__(
self,
name,
frequency,
prediction_interval,
regression_type=regression_type,
holiday_country=holiday_country,
random_seed=random_seed,
verbose=verbose,
)
self.verbose = verbose
self.random_seed = random_seed
self.kernel_initializer = kernel_initializer
self.epochs = int(epochs)
self.batch_size = int(batch_size)
self.optimizer = optimizer
self.loss = loss # negloglike, Huber, mae
self.dist = dist # normal, poisson, negbinom
def __init__(self,
name: str = "MotifSimulation",
frequency: str = 'infer',
prediction_interval: float = 0.9,
holiday_country: str = 'US',
random_seed: int = 2020,
phrase_len: str = '5',
comparison: str = 'magnitude_pct_change_sign',
shared: bool = False,
distance_metric: str = 'l2',
max_motifs: float = 50,
recency_weighting: float = 0.1,
cutoff_threshold: float = 0.9,
cutoff_minimum: int = 20,
point_method: str = 'median',
verbose: int = 1,
**kwargs):
ModelObject.__init__(
self,
name,
frequency,
prediction_interval,
holiday_country=holiday_country,
random_seed=random_seed,
)
self.phrase_len = phrase_len
self.comparison = comparison
self.shared = shared
self.distance_metric = distance_metric
self.max_motifs = max_motifs
self.recency_weighting = recency_weighting
self.cutoff_threshold = cutoff_threshold
self.cutoff_minimum = cutoff_minimum
self.point_method = point_method
def __init__(self,
name: str = "SeasonalNaive",
frequency: str = 'infer',
prediction_interval: float = 0.9,
holiday_country: str = 'US',
random_seed: int = 2020,
verbose: int = 0,
lag_1: int = 7,
lag_2: int = None,
method: str = 'LastValue',
**kwargs):
ModelObject.__init__(
self,
name,
frequency,
prediction_interval,
holiday_country=holiday_country,
random_seed=random_seed,
verbose=verbose,
)
self.lag_1 = abs(int(lag_1))
self.lag_2 = lag_2
if str(self.lag_2).isdigit():
self.lag_2 = abs(int(self.lag_2))
if str(self.lag_2) == str(self.lag_1):
self.lag_2 = 1
self.method = method
def __init__(
self,
name: str = "GluonTS",
frequency: str = 'infer',
prediction_interval: float = 0.9,
regression_type: str = None,
holiday_country: str = 'US',
random_seed: int = 2020,
verbose: int = 0,
gluon_model: str = 'DeepAR',
epochs: int = 20,
learning_rate: float = 0.001,
context_length=10,
forecast_length: int = 14,
):
ModelObject.__init__(
self,
name,
frequency,
prediction_interval,
regression_type=regression_type,
holiday_country=holiday_country,
random_seed=random_seed,
verbose=verbose,
)
self.gluon_model = gluon_model
if self.gluon_model == 'NPTS':
self.epochs = 20
self.learning_rate = 0.001
else:
self.epochs = epochs
self.learning_rate = learning_rate
self.context_length = context_length
self.forecast_length = forecast_length
def __init__(self,
name: str = "LastValueNaive",
frequency: str = 'infer',
prediction_interval: float = 0.9,
holiday_country: str = 'US',
random_seed: int = 2020):
ModelObject.__init__(self,
name,
frequency,
prediction_interval,
holiday_country=holiday_country,
random_seed=random_seed)
def __init__(self,
name: str = "ZeroesNaive",
frequency: str = 'infer',
prediction_interval: float = 0.9,
holiday_country: str = 'US',
random_seed: int = 2020,
verbose: int = 0):
ModelObject.__init__(self,
name,
frequency,
prediction_interval,
holiday_country=holiday_country,
random_seed=random_seed,
verbose=verbose)
def __init__(self,
name: str = "AverageValueNaive",
frequency: str = 'infer',
prediction_interval: float = 0.9,
holiday_country: str = 'US',
random_seed: int = 2020,
verbose: int = 0,
method: str = 'Median'):
ModelObject.__init__(self,
name,
frequency,
prediction_interval,
holiday_country=holiday_country,
random_seed=random_seed,
verbose=verbose)
self.method = method
def __init__(self,
name: str = "FBProphet",
frequency: str = 'infer',
prediction_interval: float = 0.9,
holiday: bool = False,
regression_type: str = None,
holiday_country: str = 'US',
random_seed: int = 2020,
verbose: int = 0):
ModelObject.__init__(self,
name,
frequency,
prediction_interval,
regression_type=regression_type,
holiday_country=holiday_country,
random_seed=random_seed,
verbose=verbose)
self.holiday = holiday