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plot_series

plot_series(
    df=None,
    forecasts_df=None,
    ids=None,
    plot_random=True,
    max_ids=8,
    models=None,
    level=None,
    max_insample_length=None,
    plot_anomalies=False,
    engine="matplotlib",
    palette=None,
    id_col="unique_id",
    time_col="ds",
    target_col="y",
    seed=0,
    resampler_kwargs=None,
    ax=None,
)
Plot forecasts and insample values. Parameters:
NameTypeDescriptionDefault
dfpandas or polars DataFrameDataFrame with columns [id_col, time_col, target_col]. Defaults to None.None
forecasts_dfpandas or polars DataFrameDataFrame with columns [id_col, time_col] and models. Defaults to None.None
idslist of strTime Series to plot. If None, time series are selected randomly. Defaults to None.None
plot_randomboolSelect time series to plot randomly. Defaults to True.True
max_idsintMaximum number of ids to plot. Defaults to 8.8
modelslist of strModels to plot. Defaults to None.None
levellist of floatPrediction intervals to plot. Defaults to None.None
max_insample_lengthintMaximum number of train/insample observations to be plotted. Defaults to None.None
plot_anomaliesboolPlot anomalies for each prediction interval. Defaults to False.False
enginestrLibrary used to plot. ‘plotly’, ‘plotly-resampler’ or ‘matplotlib’. Defaults to ‘matplotlib’.‘matplotlib’
palettestrName of the matplotlib colormap to use for the plots. If None, uses the current style. Defaults to None.None
id_colstrColumn that identifies each serie. Defaults to ‘unique_id’.‘unique_id’
time_colstrColumn that identifies each timestep, its values can be timestamps or integers. Defaults to ‘ds’.‘ds’
target_colstrColumn that contains the target. Defaults to ‘y’.‘y’
seedintSeed used for the random number generator. Only used if plot_random is True. Defaults to 0.0
resampler_kwargsdictKeyword 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.None
axmatplotlib axes, array of matplotlib axes or plotly FigureObject where plots will be added. Defaults to None.None
Returns:
TypeDescription
matplotlib or plotly figure: Plot’s figure
from utilsforecast.data import generate_series

level = [80, 95]
series = generate_series(4, freq='D', equal_ends=True, with_trend=True, n_models=2, level=level)
test_pd = series.groupby('unique_id', observed=True).tail(10).copy()
train_pd = series.drop(test_pd.index)

plt.style.use('ggplot')
fig = plot_series(
    train_pd,
    forecasts_df=test_pd,
    ids=[0, 3],
    plot_random=False,
    level=level,    
    max_insample_length=50,
    engine='matplotlib',
    plot_anomalies=True,
)
fig.savefig('imgs/plotting.png', bbox_inches='tight')