tsa.stattools.acovf()
statsmodels.tsa.stattools.acovf
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statsmodels.tsa.stattools.acovf(x, unbiased=False, demean=True, fft=False, missing='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.
Returns: acovf : array
autocovariance function
References
[R117] Parzen, E., 1963. On spectral analysis with missing observations and amplitude modulation. Sankhya: The Indian Journal of Statistics, Series A, pp.383-392.
© 2009–2012 Statsmodels Developers
© 2006–2008 Scipy Developers
© 2006 Jonathan E. Taylor
Licensed under the 3-clause BSD License.
http://www.statsmodels.org/stable/generated/statsmodels.tsa.stattools.acovf.html