class_docstr: Forecast time series data with a piecewise linear trend, fitted via prophet.
  
  The forecaster uses Facebook's prophet algorithm [1]_ and extracts the piecewise
  linear trend from it. Only hyper-parameters relevant for the trend modelling are
  exposed via the constructor.
  
  Seasonalities are set to additive and "auto" detection in prophet,
  which means that yearly, weekly and daily seasonality are automatically detected,
  and included in the model if present, using prophet's default settings.
  
  For more granular control of components or seasonality, use
  ``sktime.forecasting.fbprophet.Prophet`` directly.
  
  Data can be passed in one of the sktime compatible formats,
  naming a column `ds` such as in the prophet package is not necessary.
  
  Unlike vanilla `prophet`, also supports integer/range and period index:
  * integer/range index is interpreted as days since Jan 1, 2000
  * `PeriodIndex` is converted using the `pandas` method `to_timestamp`
  
  Parameters
  ----------
  changepoints: list or None, default=None
      List of dates at which to include potential changepoints. If
      not specified, potential changepoints are selected automatically.
  n_changepoints: int, default=25
      Number of potential changepoints to include. Not used
      if input `changepoints` is supplied. If `changepoints` is not supplied,
      then n_changepoints potential changepoints are selected uniformly from
      the first `changepoint_range` proportion of the history.
  changepoint_range: float, default=0.8
      Proportion of history in which trend changepoints will
      be estimated. Defaults to 0.8 for the first 80%. Not used if
      `changepoints` is specified.
  changepoint_prior_scale: float, default=0.05
      Parameter modulating the flexibility of the
      automatic changepoint selection. Large values will allow many
      changepoints, small values will allow few changepoints.
      Recommended to take values within [0.001,0.5].
  
  References
  ----------
  .. [1] https://facebook.github.io/prophet
  
  Examples
  --------
  >>> from sktime.datasets import load_airline
  >>> from sktime.forecasting.trend import ProphetPiecewiseLinearTrendForecaster
  >>> from sktime.forecasting.base import ForecastingHorizon
  >>> from sktime.split import temporal_train_test_split
  >>> y =load_airline().to_timestamp(freq='M')
  >>> y_train, y_test = temporal_train_test_split(y)
  >>> fh = ForecastingHorizon(y.index, is_relative=False)
  >>> forecaster =  ProphetPiecewiseLinearTrendForecaster() # doctest: +SKIP
  >>> forecaster.fit(y_train) # doctest: +SKIP
  ProphetPiecewiseLinearTrendForecaster(...)
  >>> y_pred = forecaster.predict(fh) # doctest: +SKIP
code: def _instantiate_model(self):
      self._forecaster = self._ModelClass(
          growth=self.growth,
          changepoints=self.changepoints,
          n_changepoints=self.n_changepoints,
          changepoint_range=self.changepoint_range,
          yearly_seasonality=self.yearly_seasonality,
          weekly_seasonality=self.weekly_seasonality,
          daily_seasonality=self.daily_seasonality,
          holidays=self.holidays,
          seasonality_mode=self.seasonality_mode,
          seasonality_prior_scale=float(self.seasonality_prior_scale),
          holidays_prior_scale=float(self.holidays_prior_scale),
          changepoint_prior_scale=float(self.changepoint_prior_scale),
          mcmc_samples=self.mcmc_samples,
          interval_width=1 - self.alpha,
          uncertainty_samples=self.uncertainty_samples,
          stan_backend=self.stan_backend,
      )
      return self
  
file_path: sktime/sktime/forecasting/trend/_pwl_trend_forecaster.py
imports: import pandas
  from sktime.forecasting.base._base import DEFAULT_ALPHA
  from sktime.forecasting.base.adapters import _ProphetAdapter
label_desc: Use image node_id 2 for calling the ProphetPiecewiseLinearTrendForecaster obj's underlying member method code with example usage: obj._instantiate_model() and returns: self