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:
- xarray_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.
- filtarray_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_sidedbool,
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 results are obtained by first estimating the trend by applying a convolution filter to the data. The trend is then removed from the series and the average of this de-trended series for each period is the returned seasonal component.