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tsa.stattools.acovf()

statsmodels.tsa.stattools.acovf

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

Autocovariance for 1D

Parameters:

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

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