plot_interval

plot_interval(ax, interval_df)

Plot prediction intervals on an existing matplotlib axes.

This function overlays prediction intervals on an existing plot to visualize forecast uncertainty.

Parameters:

axmatplotlib.axes.Axes

The axes to add the prediction intervals to.

interval_dfpd.DataFrame

A multi-index DataFrame containing prediction intervals.

Returns:

axmatplotlib.axes.Axes

The matplotlib axes with the prediction intervals added.

Examples

>>> import pandas as pd
>>> import numpy as np
>>> from sktime.datasets import load_airline
>>> from sktime.forecasting.naive import NaiveForecaster
>>> from sktime.split import temporal_train_test_split
>>> from sktime.forecasting.base import ForecastingHorizon
>>> from sktime.utils.plotting import plot_series, plot_interval

>>> data = load_airline()
>>> y_train, y_test = temporal_train_test_split(data, test_size=12)

>>> forecaster = NaiveForecaster(strategy="last")
>>> _ = forecaster.fit(y_train)

>>> fh = ForecastingHorizon(y_test.index, is_relative=False)
>>> interval_df = forecaster.predict_interval(fh=fh)

>>> y_train.index = y_train.index.to_timestamp()
>>> y_test.index = y_test.index.to_timestamp()
>>> interval_df.index = interval_df.index.to_timestamp()

>>> fig, ax = plot_series(
...     y_train, y_test, labels=["Train", "Test"],
...     pred_interval=interval_df,
... )  
>>> plot_interval(ax, interval_df)  

>>> ax.set_title('Predictions with Confidence Intervals')  
>>> ax.set_xlabel('Date')  
>>> ax.set_ylabel('Passengers')