statsmodels.tsa.seasonal.seasonal_decompose

statsmodels.tsa.seasonal.seasonal_decompose(x, model='additive', filt=None, period=None, two_sided=True, extrapolate_trend=0)[source]

Seasonal decomposition using moving averages.

Parameters:

x : array_like

Time series. If 2d, individual series are in columns. x must contain 2 complete cycles.

model : {“additive”, “multiplicative”}, optional

Type of seasonal component. Abbreviations are accepted.

filt : array_like, optional

The filter coefficients for filtering out the seasonal component. The concrete moving average method used in filtering is determined by two_sided.

period : int, optional

Period of the series. Must be used if x is not a pandas object or if the index of x does not have a frequency. Overrides default periodicity of x if x is a pandas object with a timeseries index.

two_sided : bool, optional

The moving average method used in filtering. If True (default), a centered moving average is computed using the filt. If False, the filter coefficients are for past values only.

extrapolate_trend : int or ‘freq’, optional

If set to > 0, the trend resulting from the convolution is linear least-squares extrapolated on both ends (or the single one if two_sided is False) considering this many (+1) closest points. If set to ‘freq’, use freq closest points. Setting this parameter results in no NaN values in trend or resid components.

Returns:

DecomposeResult

A object with seasonal, trend, and resid attributes.

See also

statsmodels.tsa.filters.bk_filter.bkfilter
Baxter-King filter.
statsmodels.tsa.filters.cf_filter.cffilter
Christiano-Fitzgerald asymmetric, random walk filter.
statsmodels.tsa.filters.hp_filter.hpfilter
Hodrick-Prescott filter.
statsmodels.tsa.filters.convolution_filter
Linear filtering via convolution.
statsmodels.tsa.seasonal.STL
Season-Trend decomposition using LOESS.

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.