def _fit(error, trend, seasonal, damped):
_forecaster = _ETSModel(
y,
error=error,
trend=trend,
damped_trend=damped,
seasonal=seasonal,
seasonal_periods=self.sp,
initialization_method=self.initialization_method,
initial_level=self.initial_level,
initial_trend=self.initial_trend,
initial_seasonal=self.initial_seasonal,
bounds=self.bounds,
dates=self.dates,
freq=self.freq,
missing=self.missing,
)
_fitted_forecaster = _forecaster.fit(
start_params=self.start_params,
maxiter=self.maxiter,
full_output=self.full_output,
disp=self.disp,
callback=self.callback,
return_params=self.return_params,
)
return _forecaster, _fitted_forecaster
def _fit_forecaster(self, y, X=None):
# Select model automatically
if self.auto:
# Initialise parameter ranges
if (y > 0).all():
error_range = ["add", "mul"]
else:
warnings.warn("Warning: time series is not strictly positive,"
"multiplicative components are ommitted")
error_range = ["add"]
if self.allow_multiplicative_trend and (y > 0).all():
trend_range = ["add", "mul", None]
else:
trend_range = ["add", None]
if self.sp <= 1 or self.sp is None:
seasonal_range = [None]
elif (y > 0).all():
seasonal_range = ["add", "mul", None]
else:
seasonal_range = ["add", None]
damped_range = [True, False]
# Check information criterion input
if self.information_criterion not in ["aic", "bic", "aicc"]:
raise ValueError(
"information criterion must either be aic, bic or aicc")
# Fit model, adapted from:
# https://github.com/robjhyndman/forecast/blob/master/R/ets.R
# Initialise iterator
def _iter(error_range, trend_range, seasonal_range, damped_range):
for error, trend, seasonal, damped in product(
error_range, trend_range, seasonal_range,
damped_range):
if trend is None and damped:
continue
if self.restrict:
if error == "add" and (trend == "mul"
or seasonal == "mul"):
continue
if error == "mul" and trend == "mul" and seasonal == "add":
continue
if self.additive_only and (error == "mul"
or trend == "mul"
or seasonal == "mul"):
continue
yield error, trend, seasonal, damped
# Fit function
def _fit(error, trend, seasonal, damped):
_forecaster = _ETSModel(
y,
error=error,
trend=trend,
damped_trend=damped,
seasonal=seasonal,
seasonal_periods=self.sp,
initialization_method=self.initialization_method,
initial_level=self.initial_level,
initial_trend=self.initial_trend,
initial_seasonal=self.initial_seasonal,
bounds=self.bounds,
dates=self.dates,
freq=self.freq,
missing=self.missing,
)
_fitted_forecaster = _forecaster.fit(
start_params=self.start_params,
maxiter=self.maxiter,
full_output=self.full_output,
disp=self.disp,
callback=self.callback,
return_params=self.return_params,
)
return _forecaster, _fitted_forecaster
# Fit models
_fitted_results = Parallel(n_jobs=self.n_jobs)(
delayed(_fit)(error, trend, seasonal, damped)
for error, trend, seasonal, damped in _iter(
error_range, trend_range, seasonal_range, damped_range))
# Store IC values for each model in a list
# Ignore infinite likelihood models if ignore_inf_ic is True
_ic_list = []
for result in _fitted_results:
ic = getattr(result[1], self.information_criterion)
if self.ignore_inf_ic and np.isinf(ic):
_ic_list.append(np.nan)
else:
_ic_list.append(ic)
# Select best model based on information criterion
if np.all(np.isnan(_ic_list)) or len(_ic_list) == 0:
# if all models have infinite IC raise an error
raise ValueError("None of the fitted models have finite %s" %
self.information_criterion)
else:
# Get index of best model
_index = np.nanargmin(_ic_list)
# Update best model
self._forecaster = _fitted_results[_index][0]
self._fitted_forecaster = _fitted_results[_index][1]
else:
self._forecaster = _ETSModel(
y,
error=self.error,
trend=self.trend,
damped_trend=self.damped_trend,
seasonal=self.seasonal,
seasonal_periods=self.sp,
initialization_method=self.initialization_method,
initial_level=self.initial_level,
initial_trend=self.initial_trend,
initial_seasonal=self.initial_seasonal,
bounds=self.bounds,
dates=self.dates,
freq=self.freq,
missing=self.missing,
)
self._fitted_forecaster = self._forecaster.fit(
start_params=self.start_params,
maxiter=self.maxiter,
full_output=self.full_output,
disp=self.disp,
callback=self.callback,
return_params=self.return_params,
)
def _fit_forecaster(self, y, X=None):
# Select model automatically
if self.auto:
# Initialise parameter ranges
error_range = ["add", "mul"]
if self.allow_multiplicative_trend:
trend_range = ["add", "mul", None]
else:
trend_range = ["add", None]
if self.sp <= 1 or self.sp is None:
seasonal_range = [None]
else:
seasonal_range = ["add", "mul", None]
damped_range = [True, False]
# Check information criterion input
if (
self.information_criterion != "aic"
and self.information_criterion != "bic"
and self.information_criterion != "aicc"
):
raise ValueError(
"information criterion must either be aic, bic or aicc"
)
# Fit model, adapted from:
# https://github.com/robjhyndman/forecast/blob/master/R/ets.R
# Initialise iterator
def _iter(error_range, trend_range, seasonal_range, damped_range):
for error, trend, seasonal, damped in product(
error_range, trend_range, seasonal_range, damped_range
):
if trend is None and damped:
continue
if self.restrict:
if error == "add" and (trend == "mul" or seasonal == "mul"):
continue
if error == "mul" and trend == "mul" and seasonal == "add":
continue
if self.additive_only and (
error == "mul" or trend == "mul" or seasonal == "mul"
):
continue
yield error, trend, seasonal, damped
# Fit function
def _fit(error, trend, seasonal, damped):
_forecaster = _ETSModel(
y,
error=error,
trend=trend,
damped_trend=damped,
seasonal=seasonal,
seasonal_periods=self.sp,
initialization_method=self.initialization_method,
initial_level=self.initial_level,
initial_trend=self.initial_trend,
initial_seasonal=self.initial_seasonal,
bounds=self.bounds,
dates=self.dates,
freq=self.freq,
missing=self.missing,
)
_fitted_forecaster = _forecaster.fit(
start_params=self.start_params,
maxiter=self.maxiter,
full_output=self.full_output,
disp=self.disp,
callback=self.callback,
return_params=self.return_params,
)
return _forecaster, _fitted_forecaster
# Fit models
_fitted_results = Parallel(n_jobs=self.n_jobs)(
delayed(_fit)(error, trend, seasonal, damped)
for error, trend, seasonal, damped in _iter(
error_range, trend_range, seasonal_range, damped_range
)
)
# Select best model based on information criterion
# Get index of best model
_index = np.argmin(
[
getattr(result[1], self.information_criterion)
for result in _fitted_results
]
)
# Update best model
self._forecaster = _fitted_results[_index][0]
self._fitted_forecaster = _fitted_results[_index][1]
else:
self._forecaster = _ETSModel(
y,
error=self.error,
trend=self.trend,
damped_trend=self.damped_trend,
seasonal=self.seasonal,
seasonal_periods=self.sp,
initialization_method=self.initialization_method,
initial_level=self.initial_level,
initial_trend=self.initial_trend,
initial_seasonal=self.initial_seasonal,
bounds=self.bounds,
dates=self.dates,
freq=self.freq,
missing=self.missing,
)
self._fitted_forecaster = self._forecaster.fit(
start_params=self.start_params,
maxiter=self.maxiter,
full_output=self.full_output,
disp=self.disp,
callback=self.callback,
return_params=self.return_params,
)
