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Приведенный ниже код перебирает примерно 120 переменных в vars . В настоящее время заполнение файла занимает около 80 секунд, что довольно долго. Мне было любопытно, видел ли кто-нибудь какие-либо низко висящие плоды с точки зрения ускорения моего кода. У меня 32 ядра, но изменение числа ядер = 32 для допараллеля не принесло мне большой пользы. Возможно, переписать цикл for в «применить»?

 # rm(list = ls()) #clears the workspace   library(plyr) library(ggplot2) library(scales) library(foreach)  library(doParallel) library(forecast)  # set up options(scipen = 999) #removes scientific notation registerDoParallel(cores = 32)   # read data proj_path = "P:/R" Macro lt;- read.csv("P:/Earnest/Old/R/Input.csv")  # prep data source("P:/Earnest/Old/R/VarTS.R") #Calls variables  cbind.fill lt;- function(...){  nm lt;- list(...)   nm lt;- lapply(nm, as.matrix)  n lt;- max(sapply(nm, nrow))   do.call(cbind, lapply(nm, function (x)   rbind(x, matrix(, n-nrow(x), ncol(x)))))  }  len lt;- nrow(Macro)  # Loads all variable names from Macro and Macro2 vars_macro = names(Macro)[!names(Macro) %in% c("qtrs", "y", "s1", "s2", "s3", "date")] #Returns names in Macro not in "qtrs", "y", "s1", "s2", "s3" vars_macro2 = names(Macro2)[!names(Macro2) %in% c("y", "s1", "s2", "s3")] vars_macro3 = names(Macro3)[!names(Macro3) %in% c("y", "s1", "s2", "s3")] vars_macroall = names(Macroall)[!names(Macroall) %in% c("y", "s1", "s2", "s3")] vars = c(vars_macro, vars_macro2, vars_macro3) consensus = Macro[[2]][len]  fit lt;- list() output lt;- list() forecast lt;- list()   for(m in vars){  thedata lt;- get(m)  output[[m]] lt;- list() # treat output as a list-of-lists   fit[[m]] lt;- list() # treat fit as a list-of-lists   for(z in rev(1:6)) {  tryCatch( #added tryCatch because for loop was breaking on error around number of dimensions  expr = {  x lt;- thedata[1:(len-z),1:1]  x lt;- ts((x), start = c(2016, 3), frequency = 4)  y lt;- Macro[1:(len-z),2:2]  y lt;- ts((y), start = c(2016, 3), frequency = 4)  t lt;- thedata[(len 1-z):(len 1-z),1:1]  t lt;- ts((t), start = c(2018, 4), frequency = 4)    #fit model  fit[[m]][[z]] lt;-auto.arima(y,xreg=x,seasonal=TRUE,parallel = TRUE,num.cores = NULL) #D=1 enforces seasonality  output[[m]][[z]] lt;- forecast(fit[[m]][[z]],xreg=t)$mean  },  error = function(e){  message("* Caught an error on itertion", m)  print(e)  }  )}}  output output2 lt;- matrix(unlist(output), ncol = length(vars)) output2 lt;- output2[nrow(output2):1,]   matrixoutput lt;- output2[1:5,1:length(vars)] matrixactual lt;- matrix(Macro[(len-5):(len-1),2:2]) matrixactual lt;- c(matrixactual)   MAPE lt;- colMeans(abs((matrixactual - matrixoutput) / matrixactual)) Forecast lt;- output2[6:6,1:length(vars)] Delta lt;- ((Forecast - consensus) / consensus) LastMAPE lt;- abs(((matrixoutput[5:5,] - matrixactual[5:5])/matrixactual[5:5]))  df lt;- data.frame(vars,Forecast, MAPE, Delta) dflast lt;- data.frame(vars,Forecast, MAPE, LastMAPE, Delta )  df_macro1 = df[df$vars %in% vars_macro,] df_macro1[17:17,] lt;- NA  df lt;- df[with(df, order(MAPE)), ] #sorts after original variables df_macro2 = df[df$vars %in% vars_macro2,] df_macro2$blankVar = NA df_macro3 = df[df$vars %in% vars_macro3,] df_macro3$blankVar = NA df_macroall = df[df$vars %in% vars_macroall,] df_macroall$blankVar = NA df_macro4 = df[df$vars %in% names(Macro4),] df_macro4$blankVar = NA  df_macro5 = dflast[dflast$vars %in% vars_macroall,] df_macro5 lt;- df_macro5[with(df_macro5, order(LastMAPE)), ]  df_macro5$blankVar = NA  df_macro1 lt;- rbind.fill(df_macro1, df_macro5)  a = cbind.fill(df_macro1, df_macro2, df_macro3, df_macroall, df_macro4, df, consensus)  print(df, row.names = FALSE) write.csv(a, "P:/Earnest/Old/R/OutputTSList.csv", row.names = F, na="")  

Не уверен, что это поможет, поставьте предоставление dput(макрос).

 dput(Macro) structure(list(qtrs = structure(1:14, .Label = c("15_Q3", "15_Q4",  "16_Q1", "16_Q2", "16_Q3", "16_Q4", "17_Q1", "17_Q2", "17_Q3",  "17_Q4", "18_Q1", "18_Q2", "18_Q3", "QQ_New"), class = "factor"),   y = c(121.3, 131.1, 142.5, 156.4, 168.7, 176.2, 177, 186.6,   199.6, 208.4, 214, 226.2, 232.5, 233.3), c1372 = c(0.51798059487155,   0.605074600778554, 0.70840580233259, 0.796638389230915, 0.800936365512504,   0.862507422705653, 0.906587776772603, 0.965869599669482,   1.02057681299029, 1.0173665712577, 1.00815541019123, 1.11026857023261,   1.12549360199319, 1.17070543044674), c5244 = c(0.0158288398871533,   0.0186529717846534, 0.0335038479568057, 0.0322124481706554,   0.0473432307176583, 0.0372644166954006, 0.055124227441671,   0.0462947124597511, 0.0595395997947759, 0.079813226336006,   0.0632338684298483, 0.0359582444979424, 0.0399978873936274,   0.0363530147033467), c5640 = c(0.0695411232121069, 0.0711030107194139,   0.0768960904393596, 0.0937721113616879, 0.0912072768529112,   0.0948627915873504, 0.0898598251896699, 0.102519015439631,   0.117307571608132, 0.116512410019832, 0.112621649435311,   0.113373707050245, 0.11920732067264, 0.11385677519257), c6164 = c(0.165253620685311,   0.180939722142955, 0.204839371353829, 0.230388360169478,   0.245455819824873, 0.250222069413121, 0.267517323013963,   0.301455130772129, 0.312527568603722, 0.318684362849784,   0.336297671149745, 0.385321973576628, 0.393392171202544,   0.414026295628249), b1372 = c(0.220276379575007, 0.232259423605283,   0.239015099925248, 0.29722406784095, 0.305759227349267, 0.314812674203001,   0.373507924872403, 0.376216626537958, 0.450679682818151,   0.422160030256414, 0.398670890305128, 0.380896038096525,   0.339513818723946, 0.365265284571949), b5244 = c(0.0256963971001724,   0.0308736893223339, 0.0314727889765328, 0.0342560993718647,   0.0329261690808683, 0.0341169107838618, 0.0500316002161605,   0.057066652393088, 0.0637597978553195, 0.102100656473269,   0.109515398926193, 0.0509080775409312, 0.034576665601428,   0.037353167421955), b5640 = c(0.0610914743954476, 0.0681070468175109,   0.0680584203087885, 0.0737178858316377, 0.0657525044040775,   0.0634389081514569, 0.0655890933419926, 0.0689747904574716,   0.0653176858840394, 0.0683221933318993, 0.0692822163266979,   0.0648739229545749, 0.0613089747918081, 0.0681802570906864  ), b6164 = c(0.106769764392002, 0.117293493937739, 0.128632140410947,   0.146139699267301, 0.15999997720466, 0.170137316488036, 0.188733545209946,   0.192072924866328, 0.200314760101575, 0.206572493122192,   0.21531555211795, 0.187877279779437, 0.161952291803993, 0.160944253061549  ), v1372 = c(0.00268999015293817, 0.00312395322452212, 0.00339511453015627,   0.00345686458302532, 0.00342490795325169, 0.0036875222492476,   0.00361618758896395, 0.00355297766248592, 0.00386182842589497,   0.00356454140879668, 0.00347191673410727, 0.00363595803623375,   0.00374222181870868, 0.00371078757415556), v5244 = c(0.000480051602474059,   0.00042588395300854, 0.0005605459198973, 0.000529571165782351,   0.00057240403833901, 0.000468179333138233, 0.000653241119295764,   0.000455570432040571, 0.000535395675184177, 0.00138501873189088,   0.00114060122318986, 0.000320532455933637, 0.000333175133801828,   0.000314970929257286), v5640 = c(0.000839152227642805, 0.000878653169087127,   0.00086250329626335, 0.000928749230480325, 0.000952705037621405,   0.0009145284719627, 0.000862662166602764, 0.000861675351344781,   0.000900555099811469, 0.000846321708899047, 0.000869990227889332,   0.000889602926022706, 0.000847332392691765, 0.000745645512078392  ), v6164 = c(0.00174116886286925, 0.00190736857470478, 0.00204206516831707,   0.00208434471661794, 0.0022434016778137, 0.00215767033473045,   0.00216675412062837, 0.00211218329813293, 0.00222445645154173,   0.00210022505915819, 0.00196775099859493, 0.00205851514065652,   0.00187858647947073, 0.00198038712502613), bv1372 = c(0.00142360941776777,   0.00151188642851632, 0.00157840305106086, 0.00168297006976765,   0.00157809381382463, 0.00180299614944991, 0.00285754565464732,   0.0026777621396315, 0.00314015380649578, 0.00293231409618566,   0.00290686161843522, 0.00248890293023165, 0.00168251123284542,   0.00179265933772828), bv5244 = c(0.000582722401914161, 0.000711777965499918,   0.000761910243648493, 0.000805854835145839, 0.000736857013245957,   0.000833892120648163, 0.0013602408759186, 0.0015050207801102,   0.0016936381650882, 0.00295309680744017, 0.0031773319850428,   0.00106962198904438, 0.000593441969063344, 0.000574791244792  ), bv5640 = c(0.00115351432401665, 0.00132428243672085, 0.00136224787475921,   0.00141606633583978, 0.00116049625522213, 0.000858609082150378,   0.000908098663997447, 0.000935982449156028, 0.000899912473850066,   0.000835053614508394, 0.000872837946479594, 0.000833516238462063,   0.000726891442062557, 0.000774037355521608), bv6164 = c(0.000926271545004555,   0.00105864530300842, 0.00109611375444535, 0.00117219207771791,   0.00122508269987305, 0.00135585463133827, 0.00195926581029822,   0.00187620455518874, 0.00206572868014085, 0.00213169451258196,   0.00205259028597028, 0.00136013066654879, 0.000919533667669498,   0.0010609844820593), s1 = c(1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L,   1L, 0L, 0L, 0L, 1L, 0L), s2 = c(0L, 1L, 0L, 0L, 0L, 1L, 0L,   0L, 0L, 1L, 0L, 0L, 0L, 1L), s3 = c(0L, 0L, 1L, 0L, 0L, 0L,   1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L), date = structure(c(1L, 1L,   1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "2018-09-30", class = "factor")), class = "data.frame", row.names = c(NA,  -14L))  

Наконец, вот как определены все переменные, т. Е. Ниже приведено то, что содержится в VarTS.R (источник(«P:/Earnest/Old/R/VarTS.R») #Вызывает переменные

 #Let's define some variables  c1372 lt;- Macro["c1372"] c5244 lt;- Macro["c5244"] c5640 lt;- Macro["c5640"] c6164 lt;- Macro["c6164"] b1372 lt;- Macro["b1372"] b5244 lt;- Macro["b5244"] b5640 lt;- Macro["b5640"] b6164 lt;- Macro["b6164"] v1372 lt;- Macro["v1372"] v5244 lt;- Macro["v5244"] v5640 lt;- Macro["v5640"] v6164 lt;- Macro["v6164"] bv1372 lt;- Macro["bv1372"] bv5244 lt;- Macro["bv5244"] bv5640 lt;- Macro["bv5640"] bv6164 lt;- Macro["bv6164"]   X1372C5244C lt;- rowMeans(Macro[c("c1372", "c5244")]) X1372C5640C lt;- rowMeans(Macro[c("c1372", "c5640")]) X1372C6164C lt;- rowMeans(Macro[c("c1372", "c6164")]) X1372C1372B lt;- rowMeans(Macro[c("c1372", "b1372")]) X1372C5244B lt;- rowMeans(Macro[c("c1372", "b5244")]) X1372C5640B lt;- rowMeans(Macro[c("c1372", "b5640")]) X1372C6164B lt;- rowMeans(Macro[c("c1372", "b6164")])  X5244C5640C lt;- rowMeans(Macro[c("c5244", "c5640")]) X5244C6164C lt;- rowMeans(Macro[c("c5244", "c6164")]) X5244C1372B lt;- rowMeans(Macro[c("c5244", "b1372")]) X5244C5244B lt;- rowMeans(Macro[c("c5244", "b5244")]) X5244C5640B lt;- rowMeans(Macro[c("c5244", "b5640")]) X5244C6164B lt;- rowMeans(Macro[c("c5244", "b6164")])  X5640C6164C lt;- rowMeans(Macro[c("c5640", "c6164")]) X5640C1372B lt;- rowMeans(Macro[c("c5640", "b1372")]) X5640C5244B lt;- rowMeans(Macro[c("c5640", "b5244")]) X5640C5640B lt;- rowMeans(Macro[c("c5640", "b5640")]) X5640C6164B lt;- rowMeans(Macro[c("c5640", "b6164")])  X6164C1372B lt;- rowMeans(Macro[c("c6164", "b1372")]) X6164C5244B lt;- rowMeans(Macro[c("c6164", "b5244")]) X6164C5640B lt;- rowMeans(Macro[c("c6164", "b5640")]) X6164C6164B lt;- rowMeans(Macro[c("c6164", "b6164")])  X1372B5244B lt;- rowMeans(Macro[c("b1372", "b5244")]) X1372B5640B lt;- rowMeans(Macro[c("b1372", "b5640")]) X1372B6164B lt;- rowMeans(Macro[c("b1372", "b6164")])  X5244B5640B lt;- rowMeans(Macro[c("b5244", "b5640")]) X5244B6164B lt;- rowMeans(Macro[c("b5244", "b6164")])  X5640B6164B lt;- rowMeans(Macro[c("b5640", "b6164")])  X1372C5640C1372B lt;- rowMeans(Macro[c("c1372", "c5640", "b1372" )]) X1372C5640C5640B lt;- rowMeans(Macro[c("c1372", "c5640", "b5640" )]) X1372C5640C6164B lt;- rowMeans(Macro[c("c1372", "c5640", "b6164" )])  X1372C6164C1372B lt;- rowMeans(Macro[c("c1372", "c6164", "b1372" )]) X1372C6164C5640B lt;- rowMeans(Macro[c("c1372", "c6164", "b5640" )]) X1372C6164C6164B lt;- rowMeans(Macro[c("c1372", "c6164", "b6164" )])  X6164C5640C1372B lt;- rowMeans(Macro[c("c6164", "c5640", "b1372" )]) X6164C5640C5640B lt;- rowMeans(Macro[c("c6164", "c5640", "b5640" )]) X6164C5640C6164B lt;- rowMeans(Macro[c("c6164", "c5640", "b6164" )])  XAll3Card lt;- rowMeans(Macro[c("c1372","c5640","c6164")]) XAll3Card1372B lt;- rowMeans(Macro[c("c1372","c5640","c6164","b1372")]) XAll3Card5640B lt;- rowMeans(Macro[c("c1372","c5640","c6164","b5640")]) XAll3Card6164B lt;- rowMeans(Macro[c("c1372","c5640","c6164","b6164")])  XAll3Bank lt;- rowMeans(Macro[c("b1372","b5640","b6164")]) XAll3Bank1372C lt;- rowMeans(Macro[c("b1372","b5640","b6164","c1372")]) XAll3Bank5640C lt;- rowMeans(Macro[c("b1372","b5640","b6164","c5640")]) XAll3Bank6164C lt;- rowMeans(Macro[c("b1372","b5640","b6164","c6164")])  XAll13725640 lt;- rowMeans(Macro[c("c1372", "b1372", "c5640", "b5640")]) XAll13726164 lt;- rowMeans(Macro[c("c1372", "b1372", "c6164", "b6164")]) XAll56406164 lt;- rowMeans(Macro[c("c5640", "b5640", "c6164", "b6164")])  XAll4Card lt;- rowMeans(Macro[c("c1372", "c5244", "c5640", "c6164")]) XAll4Bank lt;- rowMeans(Macro[c("b1372", "b5244", "b5640", "b6164")]) XAll6 lt;- rowMeans(Macro[c("c1372","c5640","c6164","b1372","b5640","b6164")]) XAll8 lt;- rowMeans(Macro[c("c1372", "c5244", "c5640", "c6164","b1372", "b5244", "b5640", "b6164")])      X1372V5244V lt;- rowMeans(Macro[c("v1372", "v5244")]) X1372V5640V lt;- rowMeans(Macro[c("v1372", "v5640")]) X1372V6164V lt;- rowMeans(Macro[c("v1372", "v6164")]) X1372V1372BV lt;- rowMeans(Macro[c("v1372", "bv1372")]) X1372V5244BV lt;- rowMeans(Macro[c("v1372", "bv5244")]) X1372V5640BV lt;- rowMeans(Macro[c("v1372", "bv5640")]) X1372V6164BV lt;- rowMeans(Macro[c("v1372", "bv6164")])  X5244V5640V lt;- rowMeans(Macro[c("v5244", "v5640")]) X5244V6164V lt;- rowMeans(Macro[c("v5244", "v6164")]) X5244V1372BV lt;- rowMeans(Macro[c("v5244", "bv1372")]) X5244V5244BV lt;- rowMeans(Macro[c("v5244", "bv5244")]) X5244V5640BV lt;- rowMeans(Macro[c("v5244", "bv5640")]) X5244V6164BV lt;- rowMeans(Macro[c("v5244", "bv6164")])  X5640V6164V lt;- rowMeans(Macro[c("v5640", "v6164")]) X5640V1372BV lt;- rowMeans(Macro[c("v5640", "bv1372")]) X5640V5244BV lt;- rowMeans(Macro[c("v5640", "bv5244")]) X5640V5640BV lt;- rowMeans(Macro[c("v5640", "bv5640")]) X5640V6164BV lt;- rowMeans(Macro[c("v5640", "bv6164")])  X6164V1372BV lt;- rowMeans(Macro[c("v6164", "bv1372")]) X6164V5244BV lt;- rowMeans(Macro[c("v6164", "bv5244")]) X6164V5640BV lt;- rowMeans(Macro[c("v6164", "bv5640")]) X6164V6164BV lt;- rowMeans(Macro[c("v6164", "bv6164")])  X1372BV5244BV lt;- rowMeans(Macro[c("bv1372", "bv5244")]) X1372BV5640BV lt;- rowMeans(Macro[c("bv1372", "bv5640")]) X1372BV6164BV lt;- rowMeans(Macro[c("bv1372", "bv6164")])  X5244BV5640BV lt;- rowMeans(Macro[c("bv5244", "bv5640")]) X5244BV6164BV lt;- rowMeans(Macro[c("bv5244", "bv6164")])  X5640BV6164BV lt;- rowMeans(Macro[c("bv5640", "bv6164")])  X1372V5640V1372BV lt;- rowMeans(Macro[c("v1372", "v5640", "bv1372" )]) X1372V5640V5640BV lt;- rowMeans(Macro[c("v1372", "v5640", "bv5640" )]) X1372V5640V6164BV lt;- rowMeans(Macro[c("v1372", "v5640", "bv6164" )])  X1372V6164V1372BV lt;- rowMeans(Macro[c("v1372", "v6164", "bv1372" )]) X1372V6164V5640BV lt;- rowMeans(Macro[c("v1372", "v6164", "bv5640" )]) X1372V6164V6164BV lt;- rowMeans(Macro[c("v1372", "v6164", "bv6164" )])  X6164V5640V1372BV lt;- rowMeans(Macro[c("v6164", "v5640", "bv1372" )]) X6164V5640V5640BV lt;- rowMeans(Macro[c("v6164", "v5640", "bv5640" )]) X6164V5640V6164BV lt;- rowMeans(Macro[c("v6164", "v5640", "bv6164" )])  XAll3CardV lt;- rowMeans(Macro[c("v1372","v5640","v6164")]) XAll3Card1372BV lt;- rowMeans(Macro[c("v1372","v5640","v6164","bv1372")]) XAll3Card5640BV lt;- rowMeans(Macro[c("v1372","v5640","v6164","bv5640")]) XAll3Card6164BV lt;- rowMeans(Macro[c("v1372","v5640","v6164","bv6164")])  XAll3BankV lt;- rowMeans(Macro[c("bv1372","bv5640","bv6164")]) XAll3Bank1372V lt;- rowMeans(Macro[c("bv1372","bv5640","bv6164","v1372")]) XAll3Bank5640V lt;- rowMeans(Macro[c("bv1372","bv5640","bv6164","v5640")]) XAll3Bank6164V lt;- rowMeans(Macro[c("bv1372","bv5640","bv6164","v6164")])   XAll13725640V lt;- rowMeans(Macro[c("v1372", "bv1372", "v5640", "bv5640")]) XAll13726164V lt;- rowMeans(Macro[c("v1372", "bv1372", "v6164", "bv6164")]) XAll56406164V lt;- rowMeans(Macro[c("v5640", "bv5640", "v6164", "bv6164")])  XAll4CardV lt;- rowMeans(Macro[c("v1372", "v5244", "v5640", "v6164")]) XAll4BankV lt;- rowMeans(Macro[c("bv1372", "bv5244", "bv5640", "bv6164")]) XAll6V lt;- rowMeans(Macro[c("v1372","v5640","v6164","bv1372","bv5640","bv6164")]) XAll8V lt;- rowMeans(Macro[c("v1372", "v5244", "v5640", "v6164","bv1372", "bv5244", "bv5640", "bv6164")])     X1372C5244C lt;- data.frame(X1372C5244C) X1372C5640C lt;- data.frame(X1372C5640C) X1372C6164C lt;- data.frame(X1372C6164C) X1372C1372B lt;- data.frame(X1372C1372B) X1372C5244B lt;- data.frame(X1372C5244B) X1372C5640B lt;- data.frame(X1372C5640B) X1372C6164B lt;- data.frame(X1372C6164B)  X5244C5640C lt;- data.frame(X5244C5640C) X5244C6164C lt;- data.frame(X5244C6164C) X5244C1372B lt;- data.frame(X5244C1372B) X5244C5244B lt;- data.frame(X5244C5244B) X5244C5640B lt;- data.frame(X5244C5640B) X5244C6164B lt;- data.frame(X5244C6164B)  X5640C6164C lt;- data.frame(X5640C6164C) X5640C1372B lt;- data.frame(X5640C1372B) X5640C5244B lt;- data.frame(X5640C5244B) X5640C5640B lt;- data.frame(X5640C5640B) X5640C6164B lt;- data.frame(X5640C6164B)  X6164C1372B lt;- data.frame(X6164C1372B) X6164C5244B lt;- data.frame(X6164C5244B) X6164C5640B lt;- data.frame(X6164C5640B) X6164C6164B lt;- data.frame(X6164C6164B)  X1372B5244B lt;- data.frame(X1372B5244B) X1372B5640B lt;- data.frame(X1372B5640B) X1372B6164B lt;- data.frame(X1372B6164B)  X5244B5640B lt;- data.frame(X5244B5640B) X5244B6164B lt;- data.frame(X5244B6164B)  X5640B6164B lt;- data.frame(X5640B6164B)  X1372C5640C1372B lt;- data.frame(X1372C5640C1372B) X1372C5640C5640B lt;- data.frame(X1372C5640C5640B) X1372C5640C6164B lt;- data.frame(X1372C5640C6164B)  X1372C6164C1372B lt;- data.frame(X1372C6164C1372B) X1372C6164C5640B lt;- data.frame(X1372C6164C5640B) X1372C6164C6164B lt;- data.frame(X1372C6164C6164B)  X6164C5640C1372B lt;- data.frame(X6164C5640C1372B) X6164C5640C5640B lt;- data.frame(X6164C5640C5640B) X6164C5640C6164B lt;- data.frame(X6164C5640C6164B)  XAll3Card lt;- data.frame(XAll3Card) XAll3Card1372B lt;- data.frame(XAll3Card1372B) XAll3Card5640B lt;- data.frame(XAll3Card5640B) XAll3Card6164B lt;- data.frame(XAll3Card6164B)  XAll3Bank lt;- data.frame(XAll3Bank) XAll3Bank1372C lt;- data.frame(XAll3Bank1372C) XAll3Bank5640C lt;- data.frame(XAll3Bank5640C) XAll3Bank6164C lt;- data.frame(XAll3Bank6164C)  XAll13725640 lt;- data.frame(XAll13725640) XAll13726164 lt;- data.frame(XAll13726164) XAll56406164 lt;- data.frame(XAll56406164)  XAll4Card lt;- data.frame(XAll4Card) XAll4Bank lt;- data.frame(XAll4Bank) XAll6 lt;- data.frame(XAll6) XAll8 lt;- data.frame(XAll8)     X1372V5244V lt;- data.frame(X1372V5244V) X1372V5640V lt;- data.frame(X1372V5640V) X1372V6164V lt;- data.frame(X1372V6164V) X1372V1372BV lt;- data.frame(X1372V1372BV) X1372V5244BV lt;- data.frame(X1372V5244BV) X1372V5640BV lt;- data.frame(X1372V5640BV) X1372V6164BV lt;- data.frame(X1372V6164BV)  X5244V5640V lt;- data.frame(X5244V5640V) X5244V6164V lt;- data.frame(X5244V6164V) X5244V1372BV lt;- data.frame(X5244V1372BV) X5244V5244BV lt;- data.frame(X5244V5244BV) X5244V5640BV lt;- data.frame(X5244V5640BV) X5244V6164BV lt;- data.frame(X5244V6164BV)  X5640V6164V lt;- data.frame(X5640V6164V) X5640V1372BV lt;- data.frame(X5640V1372BV) X5640V5244BV lt;- data.frame(X5640V5244BV) X5640V5640BV lt;- data.frame(X5640V5640BV) X5640V6164BV lt;- data.frame(X5640V6164BV)  X6164V1372BV lt;- data.frame(X6164V1372BV) X6164V5244BV lt;- data.frame(X6164V5244BV) X6164V5640BV lt;- data.frame(X6164V5640BV) X6164V6164BV lt;- data.frame(X6164V6164BV)  X1372BV5244BV lt;- data.frame(X1372BV5244BV) X1372BV5640BV lt;- data.frame(X1372BV5640BV) X1372BV6164BV lt;- data.frame(X1372BV6164BV)  X5244BV5640BV lt;- data.frame(X5244BV5640BV) X5244BV6164BV lt;- data.frame(X5244BV6164BV)  X5640BV6164BV lt;- data.frame(X5640BV6164BV)  X1372V5640V1372BV lt;- data.frame(X1372V5640V1372BV) X1372V5640V5640BV lt;- data.frame(X1372V5640V5640BV) X1372V5640V6164BV lt;- data.frame(X1372V5640V6164BV)  X1372V6164V1372BV lt;- data.frame(X1372V6164V1372BV) X1372V6164V5640BV lt;- data.frame(X1372V6164V5640BV) X1372V6164V6164BV lt;- data.frame(X1372V6164V6164BV)  X6164V5640V1372BV lt;- data.frame(X6164V5640V1372BV) X6164V5640V5640BV lt;- data.frame(X6164V5640V5640BV) X6164V5640V6164BV lt;- data.frame(X6164V5640V6164BV)  XAll3CardV lt;- data.frame(XAll3CardV) XAll3Card1372BV lt;- data.frame(XAll3Card1372BV) XAll3Card5640BV lt;- data.frame(XAll3Card5640BV) XAll3Card6164BV lt;- data.frame(XAll3Card6164BV)  XAll3BankV lt;- data.frame(XAll3BankV) XAll3Bank1372V lt;- data.frame(XAll3Bank1372V) XAll3Bank5640V lt;- data.frame(XAll3Bank5640V) XAll3Bank6164V lt;- data.frame(XAll3Bank6164V)  XAll13725640V lt;- data.frame(XAll13725640V) XAll13726164V lt;- data.frame(XAll13726164V) XAll56406164V lt;- data.frame(XAll56406164V)  XAll4CardV lt;- data.frame(XAll4CardV) XAll4BankV lt;- data.frame(XAll4BankV) XAll6V lt;- data.frame(XAll6V) XAll8V lt;- data.frame(XAll8V)   s1 lt;- Macro["s1"] s2 lt;- Macro["s2"] s3 lt;- Macro["s3"] y lt;- Macro["y"]  Macro2 lt;- data.frame(y, X1372C5244C, X1372C5640C, X1372C6164C, X1372C1372B, X1372C5244B, X1372C5640B, X1372C6164B,   X5244C5640C, X5244C6164C, X5244C1372B, X5244C5244B, X5244C5640B, X5244C6164B, X5640C6164C,   X5640C1372B, X5640C5244B, X5640C5640B, X5640C6164B, X6164C1372B, X6164C5244B, X6164C5640B, X6164C6164B, X1372B5244B, X1372B5640B, X1372B6164B, X5244B5640B, X5244B6164B, X5640B6164B, X1372C5640C1372B, X1372C5640C5640B, X1372C5640C6164B, X1372C6164C1372B, X1372C6164C5640B, X1372C6164C6164B, X6164C5640C1372B, X6164C5640C5640B, X6164C5640C6164B,   XAll3Card, XAll3Card1372B, XAll3Card5640B, XAll3Card6164B, XAll3Bank, XAll3Bank1372C, XAll3Bank5640C, XAll3Bank6164C, XAll13725640, XAll13726164, XAll56406164, XAll4Card, XAll4Bank, XAll6, XAll8, s1, s2, s3)     Macro3 lt;- data.frame(y, X1372V5244V, X1372V5640V, X1372V6164V, X1372V1372BV, X1372V5244BV, X1372V5640BV, X1372V6164BV,   X5244V5640V, X5244V6164V, X5244V1372BV, X5244V5244BV, X5244V5640BV, X5244V6164BV, X5640V6164V,   X5640V1372BV, X5640V5244BV, X5640V5640BV, X5640V6164BV, X6164V1372BV, X6164V5244BV, X6164V5640BV, X6164V6164BV, X1372BV5244BV, X1372BV5640BV, X1372BV6164BV, X5244BV5640BV, X5244BV6164BV, X5640BV6164BV, X1372V5640V1372BV, X1372V5640V5640BV, X1372V5640V6164BV, X1372V6164V1372BV, X1372V6164V5640BV, X1372V6164V6164BV, X6164V5640V1372BV, X6164V5640V5640BV, X6164V5640V6164BV,   XAll3CardV, XAll3Card1372BV, XAll3Card5640BV, XAll3Card6164BV, XAll3BankV, XAll3Bank1372V, XAll3Bank5640V, XAll3Bank6164V, XAll13725640V, XAll13726164V, XAll56406164V, XAll4CardV, XAll4BankV, XAll6V, XAll8V, s1, s2, s3)  Macro4 lt;- data.frame(c5640, c6164, b5640, b6164, v5640, v6164, bv5640, bv6164,  X5640C6164C, X5640C5640B, X5640C6164B, X6164C5640B, X6164C6164B, X5640B6164B, X6164C5640C5640B, X6164C5640C6164B, XAll56406164,   X5640V6164V, X5640V5640BV, X5640V6164BV, X6164V5640BV, X6164V6164BV, X5640BV6164BV, X6164V5640V5640BV, X6164V5640V6164BV, XAll56406164V)   Macroall lt;- data.frame(y, c1372, c5640, c6164, b1372, b5640, b6164, v1372, v5640, v6164, bv1372, bv5640, bv6164,  X1372C5640C, X1372C6164C, X1372C1372B, X1372C5640B, X1372C6164B,   X5640C6164C,   X5640C1372B, X5640C5640B, X5640C6164B, X6164C1372B, X6164C5640B, X6164C6164B, X1372B5640B, X1372B6164B, X5640B6164B, X1372C5640C1372B, X1372C5640C5640B, X1372C5640C6164B, X1372C6164C1372B, X1372C6164C5640B, X1372C6164C6164B, X6164C5640C1372B, X6164C5640C5640B, X6164C5640C6164B,   XAll3Card, XAll3Card1372B, XAll3Card5640B, XAll3Card6164B, XAll3Bank, XAll3Bank1372C, XAll3Bank5640C, XAll3Bank6164C, XAll13725640, XAll13726164, XAll56406164, XAll6,  X1372V5640V, X1372V6164V, X1372V1372BV, X1372V5640BV, X1372V6164BV,   X5640V6164V,   X5640V1372BV, X5640V5640BV, X5640V6164BV, X6164V1372BV, X6164V5640BV, X6164V6164BV, X1372BV5640BV, X1372BV6164BV, X5640BV6164BV, X1372V5640V1372BV, X1372V5640V5640BV, X1372V5640V6164BV, X1372V6164V1372BV, X1372V6164V5640BV, X1372V6164V6164BV, X6164V5640V1372BV, X6164V5640V5640BV, X6164V5640V6164BV,   XAll3CardV, XAll3Card1372BV, XAll3Card5640BV, XAll3Card6164BV, XAll3BankV, XAll3Bank1372V, XAll3Bank5640V, XAll3Bank6164V, XAll13725640V, XAll13726164V, XAll56406164V, XAll6V, s1, s2, s3)  

Это auto.arima занимает большую часть времени, что означает, что оптимизации не так много. Каждая auto.arima занимает около 200 миллисекунд. 1:6 умножить 16 столбцов на 200 миллисекунд-это 19,2 секунды. На моей машине выполнение этого решения занимает около 22 секунд без включения каких-либо параллельных ядер. Не слишком потертые накладные расходы. Окончательное редактирование: С future_lapply помощью , я получил его от 22 секунд до 12 секунд на своем 2-ядерном i3. С 32-ядрами вы должны выглядеть довольно хорошо.

Самое большое изменение заключается в том, что я подмножествую ваш Macro один раз для каждого цикла временных рядов (т. Е. rev(1:6) ). Это помогает не повторять y подмножество для каждого имени varname.

Затем вместо явного цикла lapply используется цикл, который должен быть быстрее, чем for цикл.

И последнее замечание: я все еще не мог запустить ваш код, поэтому я не уверен, что это ваши ожидаемые результаты.

 library(future.apply) plan(multiprocess)  all_outputs lt;- lapply(6:1,  function (z) {  x1 lt;- ts(Macro[-z, vars], start = c(2016, 3), frequency = 4)  y1 lt;- ts(Macro[-z, 'y'], start = c(2016, 3), frequency = 4)  t1 lt;- ts(Macro[nrow(Macro)-z 1, vars], start = c(2018, 4), frequency = 4)   fits lt;-  future_lapply(x1, function(x)  auto.arima(  y = y1,  xreg = x,  seasonal = TRUE  ))  outputs lt;-  lapply(vars, function(x)  x = forecast(fits[[x]], xreg = t1[, x])$mean)   names(outputs) lt;- vars   outputs  }  )  #if you have data.table data.table::rbindlist(all_outputs) #or base works fine as well do.call(rbind, all_outputs)  

правка2: результаты

 gt; do.call(rbind, all_outputs)  c1372 c5244 c5640 c6164 b1372 b5244 b5640  [1,] 207.3153 243.6196 240.3115 195.4832 244.7843 231.6605 236.5413 [2,] 206.3054 241.976 241.5821 198.7039 243.2236 239.3933 239.69  [3,] 204.1035 244.3271 244.2875 218.5334 243.7765 239.1547 252.0341 [4,] 222.5313 242.494 234.2466 229.7297 242.8088 247.4714 242.9732 [5,] 228.2269 242.7452 222.5183 233.2577 242.0669 242.7832 242.8417 [6,] 237.058 241.8235 235.3313 242.1377 241.7233 241.8443 241.8192  b6164 v1372 v5244 v5640 v6164 bv1372 bv5244  [1,] 244.097 243.0404 242.4219 250.7803 245.2109 243.1929 230.8454 [2,] 245.59 239.6461 240.0976 249.8216 244.076 240.2225 239.4011 [3,] 248.9123 240.3455 241.308 256.1201 241.8166 239.2862 238.6731 [4,] 245.1561 240.3136 246.373 257.4908 243.991 241.2637 247.0656 [5,] 242.3743 241.8402 242.5925 252.1454 239.7479 242.9514 242.6109 [6,] 241.8072 241.3552 244.5711 237.4899 241.7374 241.8847 244.9614  bv5640 bv6164  [1,] 231.3937 241.8475 [2,] 231.5027 239.3111 [3,] 230.2004 239.2619 [4,] 230.8542 231.8769 [5,] 233.1609 236.7501 [6,] 232.2402 241.8176  

изменить: данные

 library(scales) library(foreach)  library(doParallel) library(forecast) library(future.apply)  plan(multiprocess) #for future_lapply  Macro lt;-  structure(  list(  qtrs = structure(  1:14,  .Label = c(  "15_Q3",  "15_Q4",  "16_Q1",  "16_Q2",  "16_Q3",  "16_Q4",  "17_Q1",  "17_Q2",  "17_Q3",  "17_Q4",  "18_Q1",  "18_Q2",  "18_Q3",  "QQ_New"  ),  class = "factor"  ),  y = c(  121.3,  131.1,  142.5,  156.4,  168.7,  176.2,  177,  186.6,  199.6,  208.4,  214,  226.2,  232.5,  233.3  ),  c1372 = c(  0.51798059487155,  0.605074600778554,  0.70840580233259,  0.796638389230915,  0.800936365512504,  0.862507422705653,  0.906587776772603,  0.965869599669482,  1.02057681299029,  1.0173665712577,  1.00815541019123,  1.11026857023261,  1.12549360199319,  1.17070543044674  ),  c5244 = c(  0.0158288398871533,  0.0186529717846534,  0.0335038479568057,  0.0322124481706554,  0.0473432307176583,  0.0372644166954006,  0.055124227441671,  0.0462947124597511,  0.0595395997947759,  0.079813226336006,  0.0632338684298483,  0.0359582444979424,  0.0399978873936274,  0.0363530147033467  ),  c5640 = c(  0.0695411232121069,  0.0711030107194139,  0.0768960904393596,  0.0937721113616879,  0.0912072768529112,  0.0948627915873504,  0.0898598251896699,  0.102519015439631,  0.117307571608132,  0.116512410019832,  0.112621649435311,  0.113373707050245,  0.11920732067264,  0.11385677519257  ),  c6164 = c(  0.165253620685311,  0.180939722142955,  0.204839371353829,  0.230388360169478,  0.245455819824873,  0.250222069413121,  0.267517323013963,  0.301455130772129,  0.312527568603722,  0.318684362849784,  0.336297671149745,  0.385321973576628,  0.393392171202544,  0.414026295628249  ),  b1372 = c(  0.220276379575007,  0.232259423605283,  0.239015099925248,  0.29722406784095,  0.305759227349267,  0.314812674203001,  0.373507924872403,  0.376216626537958,  0.450679682818151,  0.422160030256414,  0.398670890305128,  0.380896038096525,  0.339513818723946,  0.365265284571949  ),  b5244 = c(  0.0256963971001724,  0.0308736893223339,  0.0314727889765328,  0.0342560993718647,  0.0329261690808683,  0.0341169107838618,  0.0500316002161605,  0.057066652393088,  0.0637597978553195,  0.102100656473269,  0.109515398926193,  0.0509080775409312,  0.034576665601428,  0.037353167421955  ),  b5640 = c(  0.0610914743954476,  0.0681070468175109,  0.0680584203087885,  0.0737178858316377,  0.0657525044040775,  0.0634389081514569,  0.0655890933419926,  0.0689747904574716,  0.0653176858840394,  0.0683221933318993,  0.0692822163266979,  0.0648739229545749,  0.0613089747918081,  0.0681802570906864  ),  b6164 = c(  0.106769764392002,  0.117293493937739,  0.128632140410947,  0.146139699267301,  0.15999997720466,  0.170137316488036,  0.188733545209946,  0.192072924866328,  0.200314760101575,  0.206572493122192,  0.21531555211795,  0.187877279779437,  0.161952291803993,  0.160944253061549  ),  v1372 = c(  0.00268999015293817,  0.00312395322452212,  0.00339511453015627,  0.00345686458302532,  0.00342490795325169,  0.0036875222492476,  0.00361618758896395,  0.00355297766248592,  0.00386182842589497,  0.00356454140879668,  0.00347191673410727,  0.00363595803623375,  0.00374222181870868,  0.00371078757415556  ),  v5244 = c(  0.000480051602474059,  0.00042588395300854,  0.0005605459198973,  0.000529571165782351,  0.00057240403833901,  0.000468179333138233,  0.000653241119295764,  0.000455570432040571,  0.000535395675184177,  0.00138501873189088,  0.00114060122318986,  0.000320532455933637,  0.000333175133801828,  0.000314970929257286  ),  v5640 = c(  0.000839152227642805,  0.000878653169087127,  0.00086250329626335,  0.000928749230480325,  0.000952705037621405,  0.0009145284719627,  0.000862662166602764,  0.000861675351344781,  0.000900555099811469,  0.000846321708899047,  0.000869990227889332,  0.000889602926022706,  0.000847332392691765,  0.000745645512078392  ),  v6164 = c(  0.00174116886286925,  0.00190736857470478,  0.00204206516831707,  0.00208434471661794,  0.0022434016778137,  0.00215767033473045,  0.00216675412062837,  0.00211218329813293,  0.00222445645154173,  0.00210022505915819,  0.00196775099859493,  0.00205851514065652,  0.00187858647947073,  0.00198038712502613  ),  bv1372 = c(  0.00142360941776777,  0.00151188642851632,  0.00157840305106086,  0.00168297006976765,  0.00157809381382463,  0.00180299614944991,  0.00285754565464732,  0.0026777621396315,  0.00314015380649578,  0.00293231409618566,  0.00290686161843522,  0.00248890293023165,  0.00168251123284542,  0.00179265933772828  ),  bv5244 = c(  0.000582722401914161,  0.000711777965499918,  0.000761910243648493,  0.000805854835145839,  0.000736857013245957,  0.000833892120648163,  0.0013602408759186,  0.0015050207801102,  0.0016936381650882,  0.00295309680744017,  0.0031773319850428,  0.00106962198904438,  0.000593441969063344,  0.000574791244792  ),  bv5640 = c(  0.00115351432401665,  0.00132428243672085,  0.00136224787475921,  0.00141606633583978,  0.00116049625522213,  0.000858609082150378,  0.000908098663997447,  0.000935982449156028,  0.000899912473850066,  0.000835053614508394,  0.000872837946479594,  0.000833516238462063,  0.000726891442062557,  0.000774037355521608  ),  bv6164 = c(  0.000926271545004555,  0.00105864530300842,  0.00109611375444535,  0.00117219207771791,  0.00122508269987305,  0.00135585463133827,  0.00195926581029822,  0.00187620455518874,  0.00206572868014085,  0.00213169451258196,  0.00205259028597028,  0.00136013066654879,  0.000919533667669498,  0.0010609844820593  ),  s1 = c(1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L,  1L, 0L, 0L, 0L, 1L, 0L),  s2 = c(0L, 1L, 0L, 0L, 0L, 1L, 0L,  0L, 0L, 1L, 0L, 0L, 0L, 1L),  s3 = c(0L, 0L, 1L, 0L, 0L, 0L,  1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L),  date = structure(  c(1L, 1L,  1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L),  .Label = "2018-09-30",  class = "factor"  )  ),  class = "data.frame",  row.names = c(NA,-14L)  )  # Loads variables vars_macro lt;- c("c1372", "c5244", "c5640", "c6164", "b1372", "b5244", "b5640",   "b6164", "v1372", "v5244", "v5640", "v6164", "bv1372", "bv5244",   "bv5640", "bv6164") vars = c(vars_macro)  all_outputs lt;- lapply(6:1,  function (z) {  x1 lt;- ts(Macro[-z, vars], start = c(2016, 3), frequency = 4)  y1 lt;- ts(Macro[-z, 'y'], start = c(2016, 3), frequency = 4)  t1 lt;- ts(Macro[nrow(Macro)-z 1, vars], start = c(2018, 4), frequency = 4)   fits lt;-  future_lapply(x1, function(x)  auto.arima(  y = y1,  xreg = x,  seasonal = TRUE  ))  outputs lt;-  lapply(vars, function(x)  x = forecast(fits[[x]], xreg = t1[, x])$mean)   names(outputs) lt;- vars   outputs  }  )  data.table::rbindlist(all_outputs) do.call(rbind, all_outputs)