plot_time_series_decomposition#

anomalearn.visualizer.time_series.decomposition.plot_time_series_decomposition(original, seasonal, trend, residual, x_ticks_loc=None, x_ticks_labels=None, x_ticks_rotation: float = 0, fig_size: Tuple = (16, 16)) → None#

Plots in a single figure the original, seasonal, trend and residual.

Parameters:

  • original (array-like) – The original time series.

  • seasonal (array-like) – The seasonal component of the time series.

  • trend (array-like) – The trend component of the time series.

  • residual (array-like) – The residual component of the time series.

  • x_ticks_loc (array-like, default=None) – The location at which printing the ticks labels. These will be also the labels in case the argument x_ticks_labels is None.

  • x_ticks_labels (array-like, default=None) – The labels of the ticks on the x to be printed on the plot, they start at the first sample and end at the last sample if x_ticks_loc is None. Otherwise, they will be printed exactly at the position specified by the other argument.

  • x_ticks_rotation (float, default=0.0) – The rotation of the ticks on the x-axis.

  • fig_size (Tuple) – The dimension of the figure.

Return type:

None