etna.analysis.plot_forecast#

plot_forecast(forecast_ts: TSDataset | List[TSDataset] | Dict[str, TSDataset], test_ts: TSDataset | None = None, train_ts: TSDataset | None = None, segments: List[str] | None = None, n_train_samples: int | None = None, columns_num: int = 2, figsize: Tuple[int, int] = (10, 5), prediction_intervals: bool = False, quantiles: List[float] | None = None)[source]#

Plot of prediction for forecast pipeline.

Parameters:

  • forecast_ts (TSDataset | List[TSDataset] | Dict[str, TSDataset]) –

    there are several options:

    1. Forecasted TSDataset with timeseries data, single-forecast mode

    2. List of forecasted TSDatasets, multi-forecast mode

    3. Dictionary with forecasted TSDatasets, multi-forecast mode

  • test_ts (TSDataset | None) – TSDataset with timeseries data

  • train_ts (TSDataset | None) – TSDataset with timeseries data

  • segments (List[str] | None) – segments to plot; if not given plot all the segments from forecast_df

  • n_train_samples (int | None) – length of history of train to plot

  • columns_num (int) – number of graphics columns

  • figsize (Tuple[int, int]) – size of the figure per subplot with one segment in inches

  • prediction_intervals (bool) – if True prediction intervals will be drawn

  • quantiles (List[float] | None) – List of quantiles to draw, if isn’t set then quantiles from a given dataset will be used. In multi-forecast mode, only quantiles present in each forecast will be used.

Raises:

  • ValueError: – if the format of forecast_ts is unknown

  • ValueError: – if there is an intersection between non-equal borders

  • ValueError: – if provided quantiles are not in the datasets