orbit/template/dlt.py [672:692]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        if self._seasonality > 1:
            if full_len <= seasonality_levels.shape[1]:
                seasonal_component = seasonality_levels[:, :full_len]
            else:
                seasonality_forecast_length = full_len - seasonality_levels.shape[1]
                seasonality_forecast_matrix = torch.zeros(
                    (num_sample, seasonality_forecast_length), dtype=torch.double
                )
                seasonal_component = torch.cat(
                    (seasonality_levels, seasonality_forecast_matrix), dim=1
                )
        else:
            seasonal_component = torch.zeros((num_sample, full_len), dtype=torch.double)

        ################################################################
        # Trend Component
        ################################################################

        # calculate level component.
        # However, if predicted end of period > training period, update with out-of-samples forecast
        if full_len <= trained_len:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



orbit/template/ets.py [222:242]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        if self._seasonality > 1:
            if full_len <= seasonality_levels.shape[1]:
                seasonal_component = seasonality_levels[:, :full_len]
            else:
                seasonality_forecast_length = full_len - seasonality_levels.shape[1]
                seasonality_forecast_matrix = torch.zeros(
                    (num_sample, seasonality_forecast_length), dtype=torch.double
                )
                seasonal_component = torch.cat(
                    (seasonality_levels, seasonality_forecast_matrix), dim=1
                )
        else:
            seasonal_component = torch.zeros((num_sample, full_len), dtype=torch.double)

        ################################################################
        # Trend Component
        ################################################################

        # calculate level component.
        # However, if predicted end of period > training period, update with out-of-samples forecast
        if full_len <= trained_len:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -