M/PhV,dZddlZddlZddlmZddlm Z m Z ddl m Z ddl mZddlmZddlmZgd Zd Zd Z ddZGddZdS)z+ Seasonal Decomposition by Moving Averages N)nanmean) PandasWrapper array_like)STL)convolution_filter)MSTL)freq_to_period)rseasonal_decompose seasonal_meanDecomposeResultrc tdt|D}|jddz tdt|dddDz }t||z|}t |||z }t jt jt j ||t j ||z f|||dd\}}t j d|t j|zt j|zj }|j dkr| }||d|<t jt jt j ||t j ||z f|||dd\}}t j |dz|jdt j|zt j|zj }|j dkr| }|||dzd<|S)z Replace nan values on trend's end-points with least-squares extrapolated values with regression considering npoints closest defined points. c3lK|]/\}}tjtj|+|V0dSNnpanyisnan.0ivalss X/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/statsmodels/tsa/seasonal.py z%_extrapolate_trend..sQa1G1G rc3lK|]/\}}tjtj|+|V0dSrrrs rrz%_extrapolate_trend.."sR  46"(4..))       rN)rcond)next enumerateshapeminmaxrlinalglstsqc_arangeonesTndimsqueeze) trendnpointsfrontback front_last back_firstknextras r_extrapolate_trendr5s0 "5))  E  A    $U44R4[11       UW_d++JUD7N++J 9?? biz**BGJ4F,G,GGH eJ     DAq Yq% 258 +beAh 6 9E zQ E&5&M 9?? bi D))274*3D+E+EEF jo     DAq YtaxQ 0 0258 ;beAh F IE zQ E$(** LrcbtjfdtDS)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. cDg|]}t|ddS)Nraxis) pd_nanmean)rrperiodxs r z!seasonal_mean..Hs0MMM!Z!)V) 1555MMMr)rarrayrange)r<r;s``rr r Bs4 8MMMMMuV}}MMM N NNradditiveTc|}t|}| tt|dddd}t|dd}t|}t jt j|std|dr't j |d krtd |#|t|}|}ntd |j d d|zkr$td d|zd |j d d|H|dzd kr't j dgdg|dz zzdgz|z }nt j d|z |}t|dz} t||| } |dkr|dz }|d krt!| |dz} |dr|| z } n|| z } t#| |} |dr| t j| d z} n| t j| d z} t j| j||zdzjd|} |dr || z | z }n| | z }g}t+| | ||fdD]A\}}||||Bt3|d |d|d|dS)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. Nindex inferred_freqr<)maxdimz,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?freqr8)seasonalr,residN)columns)rHr,rIobserved)rgetattrrlenrallisfinite ValueError startswithrr r!r>repeatintrr5r meantiler)zipappendwrapr+r )r<modelfiltr; two_sidedextrapolate_trendpfreqpwnobsnsidesr, detrendedperiod_averagesrHrIresultssnames rr r KsuL E q  B ~7D11?DII1c!$$$A q66D 6"+a.. ! !IGHHH  6!q&>> /  ~  "5))EFFO  wqzAI Da%i D D()  D D D    | A:??8SEQC6A:$66#>??&HDD9S6\622D ^^a F q$ / /EF"""QJ1"5*;a*?@@ I I #Iv66O <27?;;;;27?;;;;w($&.1*<==?FH %H u$H$G 5%#%I;;4 rwwqyy{{Dw99:::: ajaj    rceZdZdZd dZedZedZedZedZ edZ ed Z dd Z 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. Nc||_||_|Jtj|}t |t jrt j||jd}||_||_ ||_ dS)Nweights)rBrf) _seasonal_trendr ones_like isinstancepdSeriesrB_weights_resid _observed)selfrLrHr,rIris r__init__zDecomposeResult.__init__sr! ?l8,,G(BI.. )8>    !rc|jS)z Observed data)rrrss rrLzDecomposeResult.observed ~rc|jS)z The estimated seasonal component)rjrvs rrHzDecomposeResult.seasonalrwrc|jS)zThe estimated trend component)rkrvs rr,zDecomposeResult.trend  {rc|jS)zThe estimated residuals)rqrvs rrIzDecomposeResult.residrzrc|jS)z)The weights used in the robust estimation)rprvs rrizDecomposeResult.weightss }rc|jjS)zNumber of observations)rrr!rvs rr`zDecomposeResult.nobss~##rTFcPddlm}ddlm}|}||r |jdfgng} | |r |jdfgngz } |jjdkr| |r |jdfgngz } n|jjdkrt|jtj r'|jj D]} | |r|j| dfgngz } n=t|jj dD]} | |r|jdd| fdfgngz } | |r |jd fgngz } | |r |jd fgngz } t|jtj tjfr:|jj d} |jjd|jj| dz f} nd|jj ddz f} |t'| dd \}}t)t+|| D]\} \}\} }|d kr|| n1|| d d|| dddt/| d|}|dkr|}| dkr |r|jn|j}|||| ||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_mplObservedr,rrHNresidualriT)sharexonone)marker linestyle)rrz#000000)colorzorderrf)pandas.plottingrstatsmodels.graphics.utilsrrrr,rHr*rmrn DataFramerJr?r!rIrirorBsubplotsrNr rWplotrM capitalize set_title set_ylabelset_xlim tight_layout)rsrLrHr,rIrirrpltseriescolrr`xlimfigaxsaxdef_namerftitles rrzDecomposeResult.plots': CBBBBB::::::kmm&&(((3;C4>:.//U:DJ()): =  " " xG z233R GFF ] ! # #$-66 =0C>FN$-,j9::BFF t}21566A?GO$-1-z:;;RFF =DJ +,,2=@DL),--b@ dnr|RY&? @ @ 4>'*D>'*DN,@,JJDDt~+A.23D<<F Qt<<<S+4Sf5E5E+F+F   'A'&VX:%%sf===fIbAAA66844D:%%(($%FFxFBLLR]E E$KKK KK      rr)TTTTF) __name__ __module__ __qualname____doc__rtpropertyrLrHr,rIrir`rrrr r s" " " " "XXXXX$$X$  LLLLLLrr )r@NNTr)rnumpyrpandasrnpandas.core.nanopsrr:statsmodels.tools.validationrrstatsmodels.tsa.stl._stlr#statsmodels.tsa.filters.filtertoolsrstatsmodels.tsa.stl.mstlrstatsmodels.tsa.tsatoolsr __all__r5r r r rrrrs4444444BBBBBBBB((((((BBBBBB))))))333333   (((VOOO   RRRRjIIIIIIIIIIr