# statsmodels.tsa.stattools.acovf¶

statsmodels.tsa.stattools.acovf(x, unbiased=False, demean=True, fft=None, missing='none', nlag=None)[source]

Autocovariance for 1D

Parameters: x (array) – Time series data. Must be 1d. unbiased (bool) – If True, then denominators is n-k, otherwise n demean (bool) – If True, then subtract the mean x from each element of x fft (bool) – If True, use FFT convolution. This method should be preferred for long time series. missing (str) – A string in [‘none’, ‘raise’, ‘conservative’, ‘drop’] specifying how the NaNs are to be treated. nlag ({int, None}) – Limit the number of autocovariances returned. Size of returned array is nlag + 1. Setting nlag when fft is False uses a simple, direct estimator of the autocovariances that only computes the first nlag + 1 values. This can be much faster when the time series is long and only a small number of autocovariances are needed. acovf – autocovariance function array

References

 [*] Parzen, E., 1963. On spectral analysis with missing observations and amplitude modulation. Sankhya: The Indian Journal of Statistics, Series A, pp.383-392.