U mdW @sPdZddlmZddlZddlZddlZddlZddlm Z m Z ddl Z ddl Z ddlZddlmZmZddlmZmZmZmZmZddlmZmZddlmZmZmZej !ej "e#\Z$Z%e j&ej 'e$d d d d d Z(e()dZ(dZ*dZ+ddddddddddddddddd d!d"d#d$d%d&d'gZ,d(d)d*d+d,d-d.d/d0d1d2d3d4d5d6d7d8d9d:d;dd?gZ-e .e-Z/e j0e,e j1e/e 2e/d@dAZ3e j4dBdCdDdEZ5ddFdGZ6GdHdIdIZ7e j89dJdKdLdMZ:e j89dNe*dOdPZ;e j89dNdQdRdSZe j89dXe+e j89dNe*dYdZZ?d[d\Z@d]d^ZAd_d`ZBdadbZCe j8jDe j89dXe+e j89dNe*dcddZEe j89dXe+e j89dNe*dedfZFdgdhZGdidjZHdkdlZIdmdnZJe j4dodpZKe j4dqdrZLe j4dsdtZMe j89dXe+e j89dNe*e j89dudve j89dwdQdxdyZNdzd{ZOd|d}ZPe j89d~ddddgddZQddZRddZSddZTe j89ddddddddgddZUddZVe j89ddddgdddgdddggddZWe j89ddddgdddgdddggddZXddZYddZZddZ[ddZ\e j89dXddge j89dNddgddZ]ddZ^ddZ_ddZ`e j89ddddge j89dXddge j89dNddgddZaddZbddZcddZde j89dddddgddńZeddDŽZfe j89dddge j89dddgdd̈́Zge j89dXddge j89dNddge j89dddgddфZhddӄZiddՄZjddׄZkdS)z4 Author: Terence L van Zyl Modified: Kevin Sheppard ) pytest_warnsN)assert_allcloseassert_almost_equal)ConvergenceWarning ValueWarning) PY_SMOOTHERS SMOOTHERSExponentialSmoothingHoltSimpleExpSmoothing)HoltWintersArgs_test_to_restricted)r to_restrictedto_unrestrictedresultszhousing-data.csvZDATET)Z index_colZ parse_datesMSaddmulNgVWD@gmV} 8@g\sG)@@gB@gHG@g'ҨX=@gt=B@gl%}E@ga+esH@gk ".?@gLB@g5[y5D@gP|zI@gr_!?@gӸ7}D@g(.E@gϚK@gV`@@g5| E@g=ϟ6F@g %M@gclA@g)[$(F@gĖMG@z2005-03-01 00:00:00z2005-06-01 00:00:00z2005-09-01 00:00:00z2005-12-01 00:00:00z2006-03-01 00:00:00z2006-06-01 00:00:00z2006-09-01 00:00:00z2006-12-01 00:00:00z2007-03-01 00:00:00z2007-06-01 00:00:00z2007-09-01 00:00:00z2007-12-01 00:00:00z2008-03-01 00:00:00z2008-06-01 00:00:00z2008-09-01 00:00:00z2008-12-01 00:00:00z2009-03-01 00:00:00z2009-06-01 00:00:00z2009-09-01 00:00:00z2009-12-01 00:00:00z2010-03-01 00:00:00z2010-06-01 00:00:00z2010-09-01 00:00:00z2010-12-01 00:00:00freqindexmodule)scopecCstjd}|d}|}tddD],}||d||d||d||<q(|dd}tjd|jddd}tj ||d d S) Nri皙?2000-1-1Mperiodsry)rname) nprandom RandomStatestandard_normalcopyrangepd date_rangeshapeSeries)rser"irr1k/home/sam/Atlas/atlas_env/lib/python3.8/site-packages/statsmodels/tsa/holtwinters/tests/test_holtwinters.pysesgs  * r3c Cs\|jd}t|}t|}t|} t|} ||d<||d<||| d<||dd||||d<||d|d|||d<td|D]d} ||| d||| d|| d|| <||| || dd||| d|| <q|dd|dd| dd<|dtd|d|d| dd<|| } ||| | | fS)zQ Simple, slow, direct implementation of double exp smoothing for testing rrN)r,r$zerosr)arange) xalphabetal0b0 nforecastnZlvalsbxhatfterrr1r1r2_simple_dbl_exp_smootherss"        02 (rCc@seZdZeddZddZddZejj ddd d d Z d d Z ddZ ejj ddZddZddZddZddZdS)TestHoltWinterscCsddddddddd d d d g }d dddddddddddg }t||}tj|jt|jd|_||_ddddddd d!d"d#d$d%d&d'd(g}d)d*d+d,d-d.d ddddddddg}t||}tj|jt|jd|_||_d/d0d1d2d3d4d5d6d7d8d9d:d;dd?d@dAdBdCdDdEdFdGdHdIdJdKdLdMg}dNdOdPdQdRdSdTdUdVdWdXdYdZd[d\d]d^d_d`dad)d*d+d,d-d.d ddddg}t||}tj|jt|jd|_||_t|_dbdcdddedfdgdhdidjg |_ dS)kN4 ({@g'֩g|@gz|@g2nzz@g[W|@g8h{@g4Rz@gK/{#dR~@gFEŠ@g#WUv@gF&@g2U`~@z1996-12-31 00:00:00z1997-12-31 00:00:00z1998-12-31 00:00:00z1999-12-31 00:00:00z2000-12-31 00:00:00z2001-12-31 00:00:00z2002-12-31 00:00:00z2003-12-31 00:00:00z2004-12-31 00:00:00z2005-12-31 00:00:00z2006-12-31 00:00:00z2007-12-31 00:00:00rg=U1@#J{/5@g477@g=,Ԛ:@g~jt:@ga<@g9>@gjt>@g />@g}8g?@gzI@@g`@@g*ÂC@g^vD@g.[D@z1990-12-31 00:00:00z1991-12-31 00:00:00z1992-12-31 00:00:00z1993-12-31 00:00:00z1994-12-31 00:00:00z1995-12-31 00:00:00g pz~p@gʩap@gLQԙJp@gj;p@gn@Xq@gG?Xq@gX$r@gB]PGr@gʅJr@g%Ts@g;5Ls@gu7Ot@g;$.u@g!3u@gL0t@g9s@g($Zs@gX֞t@gt@giƢ)u@gv@g"u@g/z@gDL$z@gkz@guy@g$)&y@g}8'x@gy@g@Sy@g@y@z1970-12-31 00:00:00z1971-12-31 00:00:00z1972-12-31 00:00:00z1973-12-31 00:00:00z1974-12-31 00:00:00z1975-12-31 00:00:00z1976-12-31 00:00:00z1977-12-31 00:00:00z1978-12-31 00:00:00z1979-12-31 00:00:00z1980-12-31 00:00:00z1981-12-31 00:00:00z1982-12-31 00:00:00z1983-12-31 00:00:00z1984-12-31 00:00:00z1985-12-31 00:00:00z1986-12-31 00:00:00z1987-12-31 00:00:00z1988-12-31 00:00:00z1989-12-31 00:00:00g^>g{%jCg&HI@gV{6?gҏB?g*| W?gfv?g?) r*r- DatetimeIndexr infer_freq oildata_oil air_ausairlivestock2_livestockaust start_params)clsdatarrIrJrKr1r1r2 setup_classs.    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FUN = "concat") %>% as.vector # innov from np.random.seed(0); np.random.randn(4) innov <- c(1.76405235, 0.40015721, 0.97873798, 2.2408932) params <- expand.grid(models, c(TRUE, FALSE)) results <- apply(params, 1, FUN = function(p) { tryCatch( simulate(ets(austourists, model = p[1], damped = as.logical(p[2])), innov = innov), error = function(e) c(NA, NA, NA, NA)) }) %>% t rownames(results) <- apply(params, 1, FUN = function(x) paste(x[1], x[2])) guS@gS\U]YJ@g('UHuP@g҇Q@g~ƅ!i@g%Ca@grto@glg@gMۿj@gI)_@gJvlp@g @gvT5Ak@guYLl>`@gq@Ȱq@g{@gOIAO@gyuN@gfc]ܒO@g48EGP@g{e@g3dV@g-`@g 'm@gGZ*oGd@gx&1V@g"~`@gtm@AAAMAAMAMMMMAANMANMMNgjMFS@g"I@gx P@gkCQ@g ui'j@g#bJ$b@g~p@geN@g.!ES@g+j0 I@g@H0P@gVCvQ@g'i@gy-e@g qs@g8ψ@gq= ףj@g_LZ@gzsj@gqq~@gsk@gVyc@gQ3y@gc('U@gQj@g\mŝa@gc]^s@geX@g vöEO@g89@O@ghUP@gEBP@g됛e@g( d?d@dAdBd dCdDdEtjdFdGdHdIdJdKd dLtjdMtjdNtjdOdPdQdRd dStjdTtjdUtjdVdWdXdYd dZd[d\tjd]d^d_d`dadbd dcdddetjdfdgdhdidjdkd dltjdmtjdntjdodpdqdrd dsdttjtjdudvtjtjtjtjd dwdxtjtjdydztjtjtjtjd d{d|tjtjd}d~tjtjtjtjd dtjtjtjdtjtjtjtjtjd dtjtjtjdtjtjtjtjtjd d }||dS)a The final state from the R model fits to get an exact comparison Obtained with this R script: library(magrittr) library(fpp2) library(forecast) concat <- function(...) { return(paste(..., sep="")) } as_dict_string <- function(named) { string <- '{' for (name in names(named)) { string <- concat(string, """, name, "": ", named[name], ", ") } string <- concat(string, '}') return(string) } get_var <- function(named, name) { if (name %in% names(named)) val <- c(named[name]) else val <- c(NaN) names(val) <- c(name) return(val) } error <- c("A", "M") trend <- c("A", "M", "N") seasonal <- c("A", "M", "N") models <- outer(error, trend, FUN = "concat") %>% outer(seasonal, FUN = "concat") %>% as.vector # innov from np.random.seed(0); np.random.randn(4) innov <- c(1.76405235, 0.40015721, 0.97873798, 2.2408932) n <- length(austourists) + 1 # print fit parameters and final states for (damped in c(TRUE, FALSE)) { print(paste("damped =", damped)) for (model in models) { state <- tryCatch((function(){ fit <- ets(austourists, model = model, damped = damped) pars <- c() # alpha, beta, gamma, phi for (name in c("alpha", "beta", "gamma", "phi")) { pars <- c(pars, get_var(fit$par, name)) } # l, b, s1, s2, s3, s4 states <- c() for (name in c("l", "b", "s1", "s2", "s3", "s4")) states <- c(states, get_var(fit$states[n,], name)) c(pars, states) })(), error = function(e) rep(NA, 10)) cat(concat(""", model, "": ", as_dict_string(state), ", ")) } } gq`?g7*Ic?g{Z:?gT_(\?g әoO@go[V:ڛ?gP @gxx|}"gV'g ptׅ*@) r8r9rrrr>s1s2s3s4g`M?gjj,?g?g;ȍS?gse&M@g{(e#?g^Ǚ@gZEI@gQ΁f"g%>.@gaCu?g&{}Fl?g/(ޥ?g(\?gdO@g4P -?g??g<ub?g'p?g{?g6۹?g1 N8?g$7?g͖n?gO@g*?g7d?gy4?g$ޮ?gtI@?gt ?g} ?g0([?g3ZFUN@g^!_?gll$?gđ;?g^_(\?gz7ظN@g,kxY?g7\ʸ?gOMȸ?g-V4?gݳN@gp[s(?rgCaj?gDtA&?g۵º?g.5GO@gZ<?giG@g=Bg C)gB;i(@g.i֥?g9Nl7?g<$?g"4_O@gl?gn#- @g+U6ޫgtEd'ghOe*@gynY?g3Y?gL@gxa @gErrr r!g@9?gY,?gOQ?g[iY@r) repetitionsr$ random_errorsr)r$rr 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