o 0GfV,@sdZddlZddlZddlmZddlm Z m Z ddl m Z ddl mZddlmZddlmZgd Zd d Zd d Z     dddZGdddZdS)z+ Seasonal Decomposition by Moving Averages N)nanmean) PandasWrapper array_like)STL)convolution_filter)MSTL)freq_to_period)rseasonal_decompose seasonal_meanDecomposeResultrc Csntddt|D}|jddtddt|dddD}t|||}t|||}tjjtjt ||t ||f|||ddd\}}t d|tj|tj|j }|j dkri| }||d|<tjjtjt ||t ||f|||ddd\}}t |d|jdtj|tj|j }|j dkr| }|||dd<|S) z Replace nan values on trend's end-points with least-squares extrapolated values with regression considering npoints closest defined points. cs(|]\}}tt|s|VqdSNnpanyZisnan.0iZvalsr8lib/python3.10/site-packages/statsmodels/tsa/seasonal.py s  z%_extrapolate_trend..rcsr r rrrrrr"s N)Zrcond)next enumerateshapeminmaxrZlinalgZlstsqZc_ZarangeZonesTndimsqueeze) trendZnpointsZfrontZbackZ front_lastZ back_firstknZextrarrr_extrapolate_trendsH  "   , r$cstfddtDS)z Return means for each period in x. period is an int that gives the number of periods per cycle. E.g., 12 for monthly. NaNs are ignored in the mean. cs"g|] }t|dddqS)NrZaxis) pd_nanmean)rrperiodxrr Hs"z!seasonal_mean..)rarrayrange)r)r(rr'rr Bsr additiveTcCsF|}t|}|durtt|dddd}t|ddd}t|}tt|s+td|dr;t |d kr;td |durN|durJt |}|}ntd |j d d|krgtd d|d |j d d|dur|dd krt dgdg|ddg|}nt d||}t|d} t||| } |dkr|d}|d krt| |d} |dr|| } n|| } t| |} |dr| tj| d d} n | tj| d d8} t| j||djd|} |dr|| | }n| | }g}t| | ||fdD]\}}||j||dqt|d |d|d|ddS)a 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 (e.g., 1 for annual, 4 for quarterly, etc). 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 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. NindexZ inferred_freqr))Zmaxdimz,This function does not handle missing valuesmrzJMultiplicative seasonality is not appropriate for zero and negative valueszxYou must specify a period or x must be a pandas object with a PeriodIndex or a DatetimeIndex with a freq not set to Nonez'x must have 2 complete cycles requires z observations. x only has z observation(s)g?rg?Zfreqr%)seasonalr!residN)columns)r1r!r2observed)rgetattrrlenrallZisfinite ValueError startswithrrrr+repeatintrr$r ZmeanZtilerzipappendwrapr r )r)ZmodelZfiltr(Z two_sidedZextrapolate_trendZpfreqZpwnobsZnsidesr!Z detrendedZperiod_averagesr1r2resultssnamerrrr KstF   &          r c@sveZdZdZdddZeddZeddZed d Zed d Z ed dZ eddZ     dddZ dS)r a Results class for seasonal decompositions Parameters ---------- observed : array_like The data series that has been decomposed. seasonal : array_like The seasonal component of the data series. trend : array_like The trend component of the data series. resid : array_like The residual component of the data series. weights : array_like, optional The weights used to reduce outlier influence. NcCsR||_||_|durt|}t|tjrtj||jdd}||_||_ ||_ dS)Nweights)r.rC) _seasonal_trendrZ ones_like isinstancepdSeriesr._weights_resid _observed)selfr5r1r!r2rDrrr__init__s   zDecomposeResult.__init__cC|jS)z Observed data)rLrMrrrr5zDecomposeResult.observedcCrO)z The estimated seasonal component)rErPrrrr1rQzDecomposeResult.seasonalcCrO)zThe estimated trend component)rFrPrrrr! rQzDecomposeResult.trendcCrO)zThe estimated residuals)rKrPrrrr2rQzDecomposeResult.residcCrO)z)The weights used in the robust estimation)rJrPrrrrDrQzDecomposeResult.weightscCs|jjS)zNumber of observations)rLrrPrrrr@szDecomposeResult.nobsTFcCs>ddlm}ddlm}|}||r|jdfgng} | |r%|jdfgng7} |jjdkr;| |r7|jdfgng7} n@|jjdkr{t|jt j r^|jj D]} | |rY|j| dfgng7} qLnt |jj dD]} | |rw|jdd| fdfgng7} qf| |r|jd fgng7} | |r|jd fgng7} t|jt j t jfr|jj d} |jjd|jj| df} n d|jj ddf} |jt| dd d \}}tt|| D]I\} \}\} }|d kr|| n|j| d dd|j| ddddt| d|}|dkr|}| dkr |r |jn|j}|||| q||S)a@ Plot estimated components Parameters ---------- observed : bool Include the observed series in the plot seasonal : bool Include the seasonal component in the plot trend : bool Include the trend component in the plot resid : bool Include the residual in the plot weights : bool Include the weights in the plot (if any) Returns ------- matplotlib.figure.Figure The figure instance that containing the plot. r)register_matplotlib_converters) _import_mplZObservedr!rr1NZresidualrDT)ZsharexoZnone)ZmarkerZ linestyle)rrz#000000)ZcolorZzorderrC)Zpandas.plottingrRZstatsmodels.graphics.utilsrSrLr!r1rrGrHZ DataFramer3r,rr2rDrIr.Zsubplotsr7rr=plotr6 capitalizeZ set_titleZ set_ylabelZset_xlimZ tight_layout)rMr5r1r!r2rDrRrSZpltZseriescolrr@ZxlimZfigZaxsZaxZdef_namerCtitlerrrrVsN          zDecomposeResult.plotr )TTTTF) __name__ __module__ __qualname____doc__rNpropertyr5r1r!r2rDr@rVrrrrr s*       r )r-NNTr)r]ZnumpyrZpandasrHZpandas.core.nanopsrr&Zstatsmodels.tools.validationrrZstatsmodels.tsa.stl._stlrZ#statsmodels.tsa.filters.filtertoolsrZstatsmodels.tsa.stl.mstlrZstatsmodels.tsa.tsatoolsr__all__r$r r r rrrrs(      +