pastas.plotting.plots.acf#

pastas.plotting.plots.acf(series: pandas.Series, alpha: float = 0.05, lags: int = 365, acf_options: dict | None = None, smooth_conf: bool = True, color: str = 'k', ax: pastas.typing.Axes | None = None, **kwargs) → pastas.typing.Axes#

Plot of the autocorrelation function of a time series.

Parameters:

  • series (pandas.Series) – Residual series to plot the autocorrelation function for.

  • alpha (float, optional) – Significance level to calculate the (1-alpha)-confidence intervals. For 95% confidence intervals, alpha should be 0.05.

  • lags (int, optional) – Maximum number of lags (in days) to compute the autocorrelation for.

  • acf_options (dict, optional) – Dictionary with keyword arguments passed on to pastas.stats.acf.

  • smooth_conf (bool, optional) – For irregular time series the confidence interval may be.

  • color (str, optional) – Color of the vertical autocorrelation lines.

  • ax (matplotlib.axes.Axes, optional) – Matplotlib Axes instance to plot the ACF on. A new Figure and Axes is created when no value for ax is provided.

  • **kwargs (dict, optional) – Optional keyword arguments, passed on to plt.subplots.

Returns:

ax

Return type:

matplotlib.axes.Axes

Examples

>>> res = pd.Series(index=pd.date_range(start=0, periods=1000, freq="D"),
>>>                 data=np.random.rand(1000))
>>> ps.plots.acf(res)
Raises:

  • Warning if the ACF is empty. The plot will still be created to ensure that scripts –

  • will still run when dealing with many models. –