plot_residuals(forecast_df: pandas.core.frame.DataFrame, ts: TSDataset, feature: Union[str, Literal['timestamp']] = 'timestamp', transforms: Sequence[etna.transforms.base.Transform] = (), segments: Optional[List[str]] = None, columns_num: int = 2, figsize: Tuple[int, int] = (10, 5))[source]

Plot residuals for predictions from backtest against some feature.

  • forecast_df (pandas.core.frame.DataFrame) – forecasted dataframe with timeseries data

  • ts (TSDataset) – dataframe of timeseries that was used for backtest

  • feature (Union[str, Literal['timestamp']]) – feature name to draw against residuals, if “timestamp” plot residuals against the timestamp

  • transforms (Sequence[etna.transforms.base.Transform]) – sequence of transforms to get feature column

  • segments (Optional[List[str]]) – segments to use

  • columns_num (int) – number of columns in subplots

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


ValueError: – if feature isn’t present in the dataset after applying transformations


Parameter transforms is necessary because some pipelines doesn’t save features in their forecasts, e.g. etna.ensembles pipelines.