def fit(self, series: TimeSeries) -> "ForecastingModel":
        """Fit/train the model on the provided series.

        Parameters
        ----------
        series
            A target time series. The model will be trained to forecast this time series.

        Returns
        -------
        self
            Fitted model.
        """
        if not isinstance(series, TimeSeries):
            raise_log(
                ValueError("Train `series` must be a single `TimeSeries`."),
                logger=logger,
            )
        if not len(series) >= self.min_train_series_length:
            raise_log(
                ValueError(
                    f"Train series only contains {len(series)} elements"
                    f" but {str(self)} model requires at least {self.min_train_series_length} entries"
                ),
                logger=logger,
            )
        self.training_series = series
        self._fit_called = True

        if series.has_range_index:
            self._supports_range_index
Fit/train the model on the provided series.

        Parameters
        ----------
        series
            A target time series. The model will be trained to forecast this time series.

        Returns
        -------
        self
            Fitted model.
        
darts/models/forecasting/forecasting_model.py