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)

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