def _fit_forecaster(self, y, X=None):
self._forecaster = _ExponentialSmoothing(
y,
trend=self.trend,
damped_trend=self.damped_trend,
seasonal=self.seasonal,
seasonal_periods=self.sp,
use_boxcox=self.use_boxcox,
initial_level=self.initial_level,
initial_trend=self.initial_trend,
initial_seasonal=self.initial_seasonal,
initialization_method=self.initialization_method,
)
self._fitted_forecaster = self._forecaster.fit(
smoothing_level=self.smoothing_level,
smoothing_trend=self.smoothing_trend,
smoothing_seasonal=self.smoothing_seasonal,
damping_trend=self.damping_trend,
optimized=self.optimized,
remove_bias=self.remove_bias,
start_params=self.start_params,
method=self.method,
minimize_kwargs=self.minimize_kwargs,
use_brute=self.use_brute,
)
def _fit_forecaster(self, y, X=None):
self._forecaster = _ExponentialSmoothing(
y,
trend=self.trend,
damped_trend=self.damped_trend,
seasonal=self.seasonal,
seasonal_periods=self.sp,
use_boxcox=self.use_boxcox,
initial_level=self.initial_level,
initial_trend=self.initial_trend,
initial_seasonal=self.initial_seasonal,
initialization_method=self.initialization_method,
)
self._fitted_forecaster = self._forecaster.fit()
def _fit_forecaster(self, y, X=None):
self._forecaster = _ExponentialSmoothing(
y,
trend=self.trend,
damped=self.damped,
seasonal=self.seasonal,
seasonal_periods=self.sp,
)
self._fitted_forecaster = self._forecaster.fit(
smoothing_level=self.smoothing_level,
optimized=self.optimized,
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_basinhopping=self.use_basinhopping,
)