Seasonal decomposition using moving averages
Parameters: | x : array-like
model : str {“additive”, “multiplicative”}
filt : array-like
freq : int, optional
two_sided : bool
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Returns: | results : obj
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See also
statsmodels.tsa.filters.bk_filter.bkfilter, statsmodels.tsa.filters.cf_filter.xffilter, statsmodels.tsa.filters.hp_filter.hpfilter, statsmodels.tsa.filters.convolution_filter
Notes
This is a naive decomposition. More sophisticated methods should be preferred.
The additive model is Y[t] = T[t] + S[t] + e[t]
The multiplicative model is Y[t] = T[t] * S[t] * e[t]
The seasonal component is first removed by applying a convolution filter to the data. The average of this smoothed series for each period is the returned seasonal component.