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plot_decomposition

method plot_decomposition(columns=None, title=None, legend="upper left", figsize=(900, 900), filename=None, display=True)[source]

Plot the trend, seasonality and residuals of a time series.

This plot is only available for forecast tasks.

Tip

Use atom's decompose method to remove trend and seasonality from the data.

Parameters columns: int, str, segment, sequence, dataframe or None, default=None
Selection of columns to plot. If None, the target column is selected.

title: str, dict or None, default=None
Title for the plot.

legend: str, dict or None, default="upper left"
Legend for the plot. See the user guide for an extended description of the choices.

  • If None: No legend is shown.
  • If str: Position to display the legend.
  • If dict: Legend configuration.

figsize: tuple, default=(900, 900)
Figure's size in pixels, format as (x, y).

filename: str, Path or None, default=None
Save the plot using this name. Use "auto" for automatic naming. The type of the file depends on the provided name (.html, .png, .pdf, etc...). If filename has no file type, the plot is saved as html. If None, the plot is not saved.

display: bool or None, default=True
Whether to render the plot. If None, it returns the figure.

Returns{#plot_decomposition-go.Figure or None} go.Figure or None
Plot object. Only returned if display=None.


See Also

plot_acf

Plot the autocorrelation function.

plot_pacf

Plot the partial autocorrelation function.

plot_series

Plot a data series.


Example

>>> from atom import ATOMForecaster
>>> from sktime.datasets import load_airline

>>> y = load_airline()

>>> atom = ATOMForecaster(y, random_state=1)
>>> atom.plot_decomposition()