Plotting - Nixtla

`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:

Name	Type	Description	Default
`df`	`pandas or polars DataFrame`	DataFrame with columns [`id_col`, `time_col`, `target_col`]. Defaults to None.	`None`
`forecasts_df`	`pandas or polars DataFrame`	DataFrame with columns [`id_col`, `time_col`] and models. Defaults to None.	`None`
`ids`	`list of str`	Time Series to plot. If None, time series are selected randomly. Defaults to None.	`None`
`plot_random`	`bool`	Select time series to plot randomly. Defaults to True.	`True`
`max_ids`	`int`	Maximum number of ids to plot. Defaults to 8.	`8`
`models`	`list of str`	Models to plot. Defaults to None.	`None`
`level`	`list of float`	Prediction intervals to plot. Defaults to None.	`None`
`max_insample_length`	`int`	Maximum number of train/insample observations to be plotted. Defaults to None.	`None`
`plot_anomalies`	`bool`	Plot anomalies for each prediction interval. Defaults to False.	`False`
`engine`	`str`	Library used to plot. ‘plotly’, ‘plotly-resampler’ or ‘matplotlib’. Defaults to ‘matplotlib’.	`‘matplotlib’`
`palette`	`str`	Name of the matplotlib colormap to use for the plots. If None, uses the current style. Defaults to None.	`None`
`id_col`	`str`	Column that identifies each serie. Defaults to ‘unique_id’.	`‘unique_id’`
`time_col`	`str`	Column that identifies each timestep, its values can be timestamps or integers. Defaults to ‘ds’.	`‘ds’`
`target_col`	`str`	Column that contains the target. Defaults to ‘y’.	`‘y’`
`seed`	`int`	Seed used for the random number generator. Only used if plot_random is True. Defaults to 0.	`0`
`resampler_kwargs`	`dict`	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.	`None`
`ax`	`matplotlib axes, array of matplotlib axes or plotly Figure`	Object where plots will be added. Defaults to None.	`None`

Returns:

Type	Description
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')

Customize titles and axis labels

plot_series doesn’t have parameters for titles or axis labels. Instead, it returns a matplotlib or plotly figure (depending on engine) that you can customize with that library’s API:

# matplotlib (default engine)
fig = plot_series(train_pd, forecasts_df=test_pd)
fig.suptitle('My forecasts')
for ax in fig.axes:
    ax.set_xlabel('Date')
    ax.set_ylabel('Sales')

# plotly
fig = plot_series(train_pd, forecasts_df=test_pd, engine='plotly')
fig.update_layout(title='My forecasts')

With matplotlib you can also create your own axes and pass them through the ax parameter to control the layout and styling yourself.