statsmodels.tsa.statespace.structural.UnobservedComponentsResults.plot_components

UnobservedComponentsResults.plot_components(which=None, alpha=0.05, observed=True, level=True, trend=True, seasonal=True, freq_seasonal=True, cycle=True, autoregressive=True, legend_loc='upper right', fig=None, figsize=None) [source]

Plot the estimated components of the model.

Parameters:

which ({'filtered', 'smoothed'}, or None, optional) – Type of state estimate to plot. Default is ‘smoothed’ if smoothed results are available otherwise ‘filtered’. alpha (float, optional) – The confidence intervals for the components are (1 - alpha) % level (boolean, optional) – Whether or not to plot the level component, if applicable. Default is True. trend (boolean, optional) – Whether or not to plot the trend component, if applicable. Default is True. seasonal (boolean, optional) – Whether or not to plot the seasonal component, if applicable. Default is True. freq_seasonal (boolean, optional) – Whether or not to plot the frequency domain seasonal component(s), if applicable. Default is True. cycle (boolean, optional) – Whether or not to plot the cyclical component, if applicable. Default is True. autoregressive (boolean, optional) – Whether or not to plot the autoregressive state, if applicable. Default is True. fig (Matplotlib Figure instance, optional) – If given, subplots are created in this figure instead of in a new figure. Note that the grid will be created in the provided figure using fig.add_subplot(). figsize (tuple, optional) – If a figure is created, this argument allows specifying a size. The tuple is (width, height).

Notes

If all options are included in the model and selected, this produces a 6x1 plot grid with the following plots (ordered top-to-bottom):

0. Observed series against predicted series
1. Level
2. Trend
3. Seasonal
4. Freq Seasonal
5. Cycle
6. Autoregressive

Specific subplots will be removed if the component is not present in the estimated model or if the corresponding keywork argument is set to False.

All plots contain (1 - alpha) % confidence intervals.