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, )