plot_series#

plot_series(*series, labels=None, markers=None, colors=None, title=None, x_label=None, y_label=None, ax=None, pred_interval=None)[source]#

Plot one or more time series.

This function allows you to plot one or more time series on a single figure via series. Used for making comparisons between different series.

The resulting figure includes the time series data plotted on a graph with x-axis as time by default and can be changed via x_label and y-axis as value of time series can be renamed via y_label and labels explaining the meaning of each series via labels, markers for data points via markers. You can also specify custom colors via colors for each series and add a title to the figure via title. If prediction intervals are available add them using pred_interval, they can be overlaid on the plot to visualize uncertainty.

Parameters:

seriespd.Series or iterable of pd.Series: One or more time series
labelslist, default = None: Names of series, will be displayed in figure legend
markers: list, default = None: Markers of data points, if None the marker “o” is used by default. The length of the list has to match with the number of series.
colors: list, default = None: The colors to use for plotting each series. Must contain one color per series
title: str, default = None: The text to use as the figure’s suptitle
pred_interval: pd.DataFrame, default = None: Output of forecaster.predict_interval(). Contains columns for lower and upper boundaries of confidence interval.
axmatplotlib axes, optional: Axes to plot on, if None, a new figure is created and returned

Returns:

figplt.Figure: It manages the final visual appearance and layout. Create a new figure, or activate an existing figure.
axplt.Axis: Axes containing the plot If ax was None, a new figure is created and returned If ax was not None, the same ax is returned with plot added

Examples

>>> from sktime.utils.plotting import plot_series
>>> from sktime.datasets import load_airline
>>> y = load_airline()
>>> fig, ax = plot_series(y)  