>h-SrSSKrSSKrSSKJr SSKJ r J r SSK J r SSK Jr SSKJr SSKJr /S QrS rS rSS jr"S S5rg)z+ Seasonal Decomposition by Moving Averages N)nanmean) PandasWrapper array_like)STL)convolution_filter)MSTL)freq_to_period)rseasonal_decompose seasonal_meanDecomposeResultrc B[S[U555nURSS- [S[USSS2555- n[X!-U5n[ X#U- 5n[ R R[ R[ R"X$5[ R"XB- 54XUSS9Supg[ R"SU5[ RU-[ RU-RnURS:XaUR5nXSU&[ R R[ R[ R"XS5[ R"X5- 54XUSS9Supg[ R"US-URS5[ RU-[ RU-RnURS:XaUR5nXUS-S&U$)z Replace nan values on trend's end-points with least-squares extrapolated values with regression considering npoints closest defined points. c3# UH:up[R"[R"U55(aM6Uv M< g7fNnpanyisnan.0ivalss kC:\Users\julio\OneDrive\Documentos\Trabajo\Ideas Frescas\venv\Lib\site-packages\statsmodels/tsa/seasonal.py %_extrapolate_trend..s+)ga1G) 5A Arc3# UH:up[R"[R"U55(aM6Uv M< g7frrrs rrr"s/ 166"((4.) A1rN)rcond)next enumerateshapeminmaxrlinalglstsqc_arangeonesTndimsqueeze) trendnpointsfrontback front_last back_firstknextras r_extrapolate_trendr6s "5) E  A   $U4R4[1    U_d+JU7N+J 99?? bii*BGGJ4F,GGH J    DA YYq% 2558 +beeAh 6 9 9E zzQ &5M 99?? bii )27743D+EEF     DA YYtaxQ 02558 ;beeAh F I IE zzQ $(* Lc [R"[U5Vs/sHn[XSU2SS9PM sn5$s snf)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. Nraxis)rarrayrange pd_nanmean)xperiodrs rr r Bs8 88uV}M}!Z)V) 15}M NNMs>cxUn[U5nUc[[USS5SS5n[USSS9n[U5n[R "[R "U55(d [S5eURS5(a)[R"US :*5(a [S 5eUcUb[U5nUnO [S 5eURS SU-:a"[S SU-S URS S35eUcLUS-S :Xa)[R"S/S/US- --S/-5U- nO[R"SU- U5n[U5S-n [XU 5n US:XaUS- nUS :a[!XS-5n URS5(aX - n OX - n [#X5n URS5(aU [R$"U S S9-n OU [R$"U S S9-n [R&"U R(X-S-5R(SUn URS5(aX - U - nOX- n/n[+XX4S5H3unnUR-UR/UR15US95 M5 [3US USUSUSS9$)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?freqr9)seasonalr-residN)columns)rGr-rHobserved)rgetattrrlenrallisfinite ValueError startswithrr r"r;repeatintrr6r meantiler*zipappendwrapr,r )r>modelfiltr? two_sidedextrapolate_trendpfreqpwnobsnsidesr- detrendedperiod_averagesrGrHresultssnames rr r KsL E q B ~7D1?DI1c!$A q6D 66"++a. ! !GHH  66!q&>>/  ~  "5)EFO  wwqzAI5a%i[A(() |? D   | A:?88SEQC6A:$66#>?&HD99S6\62D ^a F q /EF""QJ1"5a*?@ I I #I6O 277?;;277?;;ww(($.1*<=??FH  u$$G %#%I4 rwwqyy{Dw9: ajaj  r7c\rSrSrSrS Sjr\S5r\S5r\S5r \S5r \S 5r \S 5r SS jr S rg)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. NcX lX0lUcT[R"U5n[ U[ R 5(a[ R "XQRSS9nXPlX@l Xl g)Nweights)rAre) _seasonal_trendr ones_like isinstancepdSeriesrA_weights_resid _observed)selfrKrGr-rHris r__init__DecomposeResult.__init__sX! ?ll8,G(BII..))>>   !r7cUR$)z Observed data)rrrss rrKDecomposeResult.observed~~r7cUR$)z The estimated seasonal component)rjrws rrGDecomposeResult.seasonalryr7cUR$)zThe estimated trend component)rkrws rr-DecomposeResult.trend {{r7cUR$)zThe estimated residuals)rqrws rrHDecomposeResult.residr~r7cUR$)z)The weights used in the robust estimation)rprws rriDecomposeResult.weightss}}r7c.URR$)zNumber of observations)rrr"rws rr_DecomposeResult.nobss~~###r7c(SSKJn SSKJn U"5nU"5 U(aURS4/O/n X(aUR S4/O/- n UR RS:XaX(aUR S4/O/- n OUR RS:a[UR [R5(a<UR RH!n U U(aUR U S4/O/- n M# OK[UR RS5H%n U U(aUR SS2U 4S4/O/- n M' X(aURS 4/O/- n X(aURS 4/O/- n [UR[R[R 45(aOURRSn URR"SURR"U S- 4n OSURRSS- 4n UR%['U 5SS S 9up[)[+X55Hun unun nUS :waUR-U 5 O#UR-U S SS9 UR-U SSSS9 [/U SU5nUS:waUR15nU S:XaU(a UR2O UR4nU"U5 UR7U 5 M UR95 U$)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-rrGNresidualriT)sharexonone)marker linestyle)rrz#000000)colorzorderre)pandas.plottingrstatsmodels.graphics.utilsrrrr-rGr+rmrn DataFramerIr<r"rHrirorAsubplotsrMr!rVplotrL capitalize set_title set_ylabelset_xlim tight_layout)rsrKrGr-rHrirrpltseriescolrr_xlimfigaxsaxdef_nameretitles rrDecomposeResult.plots: C:m&(3;4>>:./UDJJ(): ==   " x z23R GF ]]  ! #$--66==00C>F$--,j9:BF1 t}}22156A?G$--1-z:;RF7 DJJ +,2=DLL),-b@ dnnr||RYY&? @ @>>''*D>>''*DNN,@,@,JJDt~~++A.23D<<F Qt<<+4S5E+F 'A'&VX:%sf=fIbA6684D:%($%FxBLLR]]E $K KK ,G  r7)rrrqrjrkrpr)TTTTF)__name__ __module__ __qualname____firstlineno____doc__rtpropertyrKrGr-rHrir_r__static_attributes__r7rr r s" "$$  Lr7r )additiveNNTr)rnumpyrpandasrnpandas.core.nanopsrr=statsmodels.tools.validationrrstatsmodels.tsa.stl._stlr#statsmodels.tsa.filters.filtertoolsrstatsmodels.tsa.stl.mstlrstatsmodels.tsa.tsatoolsr __all__r6r r r rr7rrs[4B(B)3 (VO   RjIIr7