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Data:
0.0623999591436363 0.0673999230710553 0.0760999147466414 0.0673999158700459 0.074499915794244 0.0725999133063456 0.0604999222274966 0.0660999249209322 0.0764999151680504 0.0767999095051533 0.0769999092165195 0.0709999179335117 10.8359819154429 1.26205617642799 3.66239571825825 2.46947788071442 1.10714520406324 6.58335475310772 1.97149556979767 2.334280435698 -2.54332601659813 4.01793958263306 -3.78133437401082 -0.317979846061568 0.0102718897785343 -1.25092729860763 -0.700155963789233 5.94746185533375 -4.21943896535866 0.0339173356005719 4.53904960009886 -3.46259688626591 3.90893355567736 1.31620485490087 -0.828659050187798 3.53943098957265 2.89682859418273 1.28902917402445 0.852377148177291 2.06860038524397 -2.9633056377674 3.6089269689262 1.82308335334898 -1.51103941511939 5.46562585988295 -0.644132413147318 -0.421749518113039 9.41909500288001 -6.05483250613297 6.26316203786592 6.1061754426878 1.98486913639551 1.94474939888551 9.49900339871634 0.1183519039818 8.08575085363917 4.15008198724068 2.8766386151856 9.56361825989926 6.46128841992948 5.51866978266072 2.46641004271275 2.02362383855627 4.39158791305629 3.0846546589005 2.88579769666626 -2.81067389296319 3.8462301336442 1.87916828895322 -0.9943336802728 6.1403409537484 1.28922355434657 4.21553104873813 4.51846951729731 7.67243139808268 -4.46223259853389 13.2024399740054 -0.244031876757103 5.41093176400443 3.8159915787943 2.80610198743267 10.5665637320416 2.67734693845842 1.36522945194721 7.49062940807855 1.74801742338373 4.2703015092933 5.61580033453356 3.20512098763879 5.91149113506666 7.99354266370528 5.00867951139651 2.00724027563501 10.971195617128 1.54079973406887 3.1065134315452 8.34364150707301 7.72232565827425 -1.7480410474781 15.5088148870299 -2.30566904607059 6.22888409274236 6.99346124253593 -2.88325446701002 12.5835936098068 -4.49331125710061 -4.21932556933798 3.47763297430723 -15.1602623934305 -5.34672068341061 -4.470010160039 -13.5293379673141 -1.57510981243814 -9.55988965698972 -5.39297295794674 -1.52499761685971 -8.14052182850979 -5.37127486427748 2.42984671252665 -3.50408497116221 5.117138251551 6.25215906125452 9.19563568594373 4.24938824221677 2.95387448832031 12.6178133571214 1.22875472385876 7.74809965968539 5.98957899323123 4.30017082282983 9.32158519770366 6.03625848178254 4.74482656426815 3.95397680109579 12.4097744963252 -1.19324637568364 15.2879482307578 -9.81885697627214 4.46997486430695 6.29489181794462 1.75997238228665 -1.48658629757794 0.784181263377334 -1.00008124325305 4.24357645474808 2.35213512377959 -1.97504254506926 -1.0653775346474 -5.35622063327614 5.44623239713341 2.78694146260069 -2.7377059415704 -3.59718697363833 5.46930549780257 -2.15237621817309 -4.56770152209916 1.9717584189804 -6.95021543713611 -3.58145342165933 7.91998231221258 -1.71235630304608 0.944188611634672 6.04850757155987 -5.90068291709636 3.21877052425239 4.41132377437188 0.64475864453965 4.98696080764906 0.380582102752953 4.80403142656398 -3.64219595151302 3.96653248577481 -1.93618163475807 2.54959953147041 -3.44411511575284 -1.96036850131122 7.29707303571976 -3.43439634089813 -1.36653579470006 1.60885399217194 -0.581524305989163 -3.75829179017525 5.75966851954242 -2.17148776196537 -3.58329387819147 3.48292030892493 -3.21882884148393 2.83257985033849 -0.549241047179422 0.440097080020692 5.0380454108796 -0.476799846408127 2.44966216318491 7.57640070239071 0.682426725323751 4.99683860901307 2.78306349913964 4.95154425648789 -0.883660293995149 10.1021411015655 -2.58263176041314 -0.790850868174502 3.34369988033605 9.47287447684811 -1.02516817879373 3.28001608825431 6.31270146516164 -7.83284885616155 11.8457995134525 -1.61829145177705 4.94176038091014 3.88374059882783
Include mean?
FALSE
TRUE
Box-Cox lambda transformation parameter (lambda)
1
-2.0
-1.9
-1.8
-1.7
-1.6
-1.5
-1.4
-1.3
-1.2
-1.1
-1.0
-0.9
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2.0
Degree of non-seasonal differencing (d)
0
1
2
Degree of seasonal differencing (D)
0
1
Seasonal Period (s)
1
2
3
4
6
12
Maximum AR(p) order
0
1
2
3
Maximum MA(q) order
0
1
Maximum SAR(P) order
0
1
2
Maximum SMA(Q) order
0
1
Chart options
R Code
library(lattice) if (par1 == 'TRUE') par1 <- TRUE if (par1 == 'FALSE') par1 <- FALSE par2 <- as.numeric(par2) #Box-Cox lambda transformation parameter par3 <- as.numeric(par3) #degree of non-seasonal differencing par4 <- as.numeric(par4) #degree of seasonal differencing par5 <- as.numeric(par5) #seasonal period par6 <- as.numeric(par6) #degree (p) of the non-seasonal AR(p) polynomial par7 <- as.numeric(par7) #degree (q) of the non-seasonal MA(q) polynomial par8 <- as.numeric(par8) #degree (P) of the seasonal AR(P) polynomial par9 <- as.numeric(par9) #degree (Q) of the seasonal MA(Q) polynomial armaGR <- function(arima.out, names, n){ try1 <- arima.out$coef try2 <- sqrt(diag(arima.out$var.coef)) try.data.frame <- data.frame(matrix(NA,ncol=4,nrow=length(names))) dimnames(try.data.frame) <- list(names,c('coef','std','tstat','pv')) try.data.frame[,1] <- try1 for(i in 1:length(try2)) try.data.frame[which(rownames(try.data.frame)==names(try2)[i]),2] <- try2[i] try.data.frame[,3] <- try.data.frame[,1] / try.data.frame[,2] try.data.frame[,4] <- round((1-pt(abs(try.data.frame[,3]),df=n-(length(try2)+1)))*2,5) vector <- rep(NA,length(names)) vector[is.na(try.data.frame[,4])] <- 0 maxi <- which.max(try.data.frame[,4]) continue <- max(try.data.frame[,4],na.rm=TRUE) > .05 vector[maxi] <- 0 list(summary=try.data.frame,next.vector=vector,continue=continue) } arimaSelect <- function(series, order=c(13,0,0), seasonal=list(order=c(2,0,0),period=12), include.mean=F){ nrc <- order[1]+order[3]+seasonal$order[1]+seasonal$order[3] coeff <- matrix(NA, nrow=nrc*2, ncol=nrc) pval <- matrix(NA, nrow=nrc*2, ncol=nrc) mylist <- rep(list(NULL), nrc) names <- NULL if(order[1] > 0) names <- paste('ar',1:order[1],sep='') if(order[3] > 0) names <- c( names , paste('ma',1:order[3],sep='') ) if(seasonal$order[1] > 0) names <- c(names, paste('sar',1:seasonal$order[1],sep='')) if(seasonal$order[3] > 0) names <- c(names, paste('sma',1:seasonal$order[3],sep='')) arima.out <- arima(series, order=order, seasonal=seasonal, include.mean=include.mean, method='ML') mylist[[1]] <- arima.out last.arma <- armaGR(arima.out, names, length(series)) mystop <- FALSE i <- 1 coeff[i,] <- last.arma[[1]][,1] pval [i,] <- last.arma[[1]][,4] i <- 2 aic <- arima.out$aic while(!mystop){ mylist[[i]] <- arima.out arima.out <- arima(series, order=order, seasonal=seasonal, include.mean=include.mean, method='ML', fixed=last.arma$next.vector) aic <- c(aic, arima.out$aic) last.arma <- armaGR(arima.out, names, length(series)) mystop <- !last.arma$continue coeff[i,] <- last.arma[[1]][,1] pval [i,] <- last.arma[[1]][,4] i <- i+1 } list(coeff, pval, mylist, aic=aic) } arimaSelectplot <- function(arimaSelect.out,noms,choix){ noms <- names(arimaSelect.out[[3]][[1]]$coef) coeff <- arimaSelect.out[[1]] k <- min(which(is.na(coeff[,1])))-1 coeff <- coeff[1:k,] pval <- arimaSelect.out[[2]][1:k,] aic <- arimaSelect.out$aic[1:k] coeff[coeff==0] <- NA n <- ncol(coeff) if(missing(choix)) choix <- k layout(matrix(c(1,1,1,2, 3,3,3,2, 3,3,3,4, 5,6,7,7),nr=4), widths=c(10,35,45,15), heights=c(30,30,15,15)) couleurs <- rainbow(75)[1:50]#(50) ticks <- pretty(coeff) par(mar=c(1,1,3,1)) plot(aic,k:1-.5,type='o',pch=21,bg='blue',cex=2,axes=F,lty=2,xpd=NA) points(aic[choix],k-choix+.5,pch=21,cex=4,bg=2,xpd=NA) title('aic',line=2) par(mar=c(3,0,0,0)) plot(0,axes=F,xlab='',ylab='',xlim=range(ticks),ylim=c(.1,1)) rect(xleft = min(ticks) + (0:49)/50*(max(ticks)-min(ticks)), xright = min(ticks) + (1:50)/50*(max(ticks)-min(ticks)), ytop = rep(1,50), ybottom= rep(0,50),col=couleurs,border=NA) axis(1,ticks) rect(xleft=min(ticks),xright=max(ticks),ytop=1,ybottom=0) text(mean(coeff,na.rm=T),.5,'coefficients',cex=2,font=2) par(mar=c(1,1,3,1)) image(1:n,1:k,t(coeff[k:1,]),axes=F,col=couleurs,zlim=range(ticks)) for(i in 1:n) for(j in 1:k) if(!is.na(coeff[j,i])) { if(pval[j,i]<.01) symb = 'green' else if( (pval[j,i]<.05) & (pval[j,i]>=.01)) symb = 'orange' else if( (pval[j,i]<.1) & (pval[j,i]>=.05)) symb = 'red' else symb = 'black' polygon(c(i+.5 ,i+.2 ,i+.5 ,i+.5), c(k-j+0.5,k-j+0.5,k-j+0.8,k-j+0.5), col=symb) if(j==choix) { rect(xleft=i-.5, xright=i+.5, ybottom=k-j+1.5, ytop=k-j+.5, lwd=4) text(i, k-j+1, round(coeff[j,i],2), cex=1.2, font=2) } else{ rect(xleft=i-.5,xright=i+.5,ybottom=k-j+1.5,ytop=k-j+.5) text(i,k-j+1,round(coeff[j,i],2),cex=1.2,font=1) } } axis(3,1:n,noms) par(mar=c(0.5,0,0,0.5)) plot(0,axes=F,xlab='',ylab='',type='n',xlim=c(0,8),ylim=c(-.2,.8)) cols <- c('green','orange','red','black') niv <- c('0','0.01','0.05','0.1') for(i in 0:3){ polygon(c(1+2*i ,1+2*i ,1+2*i-.5 ,1+2*i), c(.4 ,.7 , .4 , .4), col=cols[i+1]) text(2*i,0.5,niv[i+1],cex=1.5) } text(8,.5,1,cex=1.5) text(4,0,'p-value',cex=2) box() residus <- arimaSelect.out[[3]][[choix]]$res par(mar=c(1,2,4,1)) acf(residus,main='') title('acf',line=.5) par(mar=c(1,2,4,1)) pacf(residus,main='') title('pacf',line=.5) par(mar=c(2,2,4,1)) qqnorm(residus,main='') title('qq-norm',line=.5) qqline(residus) residus } if (par2 == 0) x <- log(x) if (par2 != 0) x <- x^par2 (selection <- arimaSelect(x, order=c(par6,par3,par7), seasonal=list(order=c(par8,par4,par9), period=par5))) bitmap(file='test1.png') resid <- arimaSelectplot(selection) dev.off() resid bitmap(file='test2.png') acf(resid,length(resid)/2, main='Residual Autocorrelation Function') dev.off() bitmap(file='test3.png') pacf(resid,length(resid)/2, main='Residual Partial Autocorrelation Function') dev.off() bitmap(file='test4.png') cpgram(resid, main='Residual Cumulative Periodogram') dev.off() bitmap(file='test5.png') hist(resid, main='Residual Histogram', xlab='values of Residuals') dev.off() bitmap(file='test6.png') densityplot(~resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test7.png') qqnorm(resid, main='Residual Normal Q-Q Plot') qqline(resid) dev.off() ncols <- length(selection[[1]][1,]) nrows <- length(selection[[2]][,1])-1 load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'ARIMA Parameter Estimation and Backward Selection', ncols+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Iteration', header=TRUE) for (i in 1:ncols) { a<-table.element(a,names(selection[[3]][[1]]$coef)[i],header=TRUE) } a<-table.row.end(a) for (j in 1:nrows) { a<-table.row.start(a) mydum <- 'Estimates (' mydum <- paste(mydum,j) mydum <- paste(mydum,')') a<-table.element(a,mydum, header=TRUE) for (i in 1:ncols) { a<-table.element(a,round(selection[[1]][j,i],4)) } a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'(p-val)', header=TRUE) for (i in 1:ncols) { mydum <- '(' mydum <- paste(mydum,round(selection[[2]][j,i],4),sep='') mydum <- paste(mydum,')') a<-table.element(a,mydum) } a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab') a <-table.start() a <- table.row.start(a) a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE) a <- table.row.end(a) a <- table.row.start(a) a <- table.element(a,'Description',1,TRUE) a <- table.element(a,'Link',1,TRUE) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Histogram',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Central Tendency',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_centraltendency.wasp?convertgetintopost=1&data=',paste(as.character(resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'QQ Plot',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_fitdistrnorm.wasp?convertgetintopost=1&data=',paste(as.character(resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Kernel Density Plot',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_density.wasp?convertgetintopost=1&data=',paste(as.character(resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Skewness/Kurtosis Test',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis.wasp?convertgetintopost=1&data=',paste(as.character(resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Skewness-Kurtosis Plot',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis_plot.wasp?convertgetintopost=1&data=',paste(as.character(resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Harrell-Davis Plot',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_harrell_davis.wasp?convertgetintopost=1&data=',paste(as.character(resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Bootstrap Plot -- Central Tendency',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot1.wasp?convertgetintopost=1&data=',paste(as.character(resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Blocked Bootstrap Plot -- Central Tendency',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot.wasp?convertgetintopost=1&data=',paste(as.character(resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'(Partial) Autocorrelation Plot',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_autocorrelation.wasp?convertgetintopost=1&data=',paste(as.character(resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Spectral Analysis',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_spectrum.wasp?convertgetintopost=1&data=',paste(as.character(resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Tukey lambda PPCC Plot',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_tukeylambda.wasp?convertgetintopost=1&data=',paste(as.character(resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Box-Cox Normality Plot',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_boxcoxnorm.wasp?convertgetintopost=1&data=',paste(as.character(resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <- table.element(a,'Summary Statistics',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_summary1.wasp?convertgetintopost=1&data=',paste(as.character(resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a<-table.end(a) table.save(a,file='mytable7.tab')
Compute
Summary of computational transaction
Raw Input
view raw input (R code)
Raw Output
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Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
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