statsmodels.tsa.stattools.acovf¶
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statsmodels.tsa.stattools.
acovf
(x, unbiased=False, demean=True, fft=None, missing='none', nlag=None)[source]¶ Estimate autocovariances.
Parameters: x : array_like
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.
Returns: ndarray
The estimated autocovariances.
References
[R171] Parzen, E., 1963. On spectral analysis with missing observations and amplitude modulation. Sankhya: The Indian Journal of Statistics, Series A, pp.383-392.