utilsforecast.plotting
plot_series
df
(pandas or polars DataFrame, optional): DataFrame with columns [id_col
, time_col
, target_col
]. Defaults to None.forecasts_df
(pandas or polars DataFrame, optional): DataFrame with columns [id_col
, time_col
] and models. Defaults to None.ids
(list of str, optional): Time Series to plot. If None, time series are selected randomly. Defaults to None.plot_random
(bool, optional): Select time series to plot randomly. Defaults to True.max_ids
(int, optional): Maximum number of ids to plot. Defaults to 8.models
(list of str, optional): Models to plot. Defaults to None.level
(list of float, optional): Prediction intervals to plot. Defaults to None.max_insample_length
(int, optional): Maximum number of train/insample observations to be plotted. Defaults to None.plot_anomalies
(bool, optional): Plot anomalies for each prediction interval. Defaults to False.engine
(str, optional): Library used to plot. ‘plotly’, ‘plotly-resampler’ or ‘matplotlib’. Defaults to ‘matplotlib’.palette
(str, optional): Name of the matplotlib colormap to use for the plots. If None, uses the current style. Defaults to None.id_col
(str, optional): Column that identifies each serie. Defaults to ‘unique_id’.time_col
(str, optional): Column that identifies each timestep, its values can be timestamps or integers. Defaults to ‘ds’.target_col
(str, optional): Column that contains the target. Defaults to ‘y’.seed
(int, optional): Seed used for the random number generator. Only used if plot_random is True. Defaults to 0.resampler_kwargs
(dict, optional): Keyword arguments to be passed to plotly-resampler constructor. For further custumization (“show_dash”) call the method, store the plotting object and add the extra arguments to its show_dash
method. Defaults to None. ax (matplotlib axes, array of matplotlib axes or plotly Figure, optional): Object where plots will be added. Defaults to None.matplotlib or plotly figure
: Plot’s figure