R version 2.6.0 (2007-10-03) Copyright (C) 2007 The R Foundation for Statistical Computing ISBN 3-900051-07-0 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > x <- c(96.8,87.0,96.3,107.1,115.2,106.1,89.5,91.3,97.6,100.7,104.6,94.7,101.8,102.5,105.3,110.3,109.8,117.3,118.8,131.3,125.9,133.1,147.0,145.8,164.4,149.8,137.7,151.7,156.8,180.0,180.4,170.4,191.6,199.5,218.2,217.5,205.0,194.0,199.3,219.3,211.1,215.2,240.2,242.2,240.7,255.4,253.0,218.2,203.7,205.6,215.6,188.5,202.9,214.0,230.3,230.0,241.0,259.6,247.8,270.3) > par10 = 'FALSE' > par9 = '0' > par8 = '1' > par7 = '1' > par6 = '2' > par5 = '12' > par4 = '1' > par3 = '0' > par2 = '1.0' > par1 = '12' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Prof. Dr. P. Wessa > #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > par1 <- as.numeric(par1) #cut off periods > par2 <- as.numeric(par2) #lambda > 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) #p > par7 <- as.numeric(par7) #q > par8 <- as.numeric(par8) #P > par9 <- as.numeric(par9) #Q > if (par10 == 'TRUE') par10 <- TRUE > if (par10 == 'FALSE') par10 <- FALSE > if (par2 == 0) x <- log(x) > if (par2 != 0) x <- x^par2 > lx <- length(x) > first <- lx - 2*par1 > nx <- lx - par1 > nx1 <- nx + 1 > fx <- lx - nx > if (fx < 1) { + fx <- par5 + nx1 <- lx + fx - 1 + first <- lx - 2*fx + } > first <- 1 > if (fx < 3) fx <- round(lx/10,0) > (arima.out <- arima(x[1:nx], order=c(par6,par3,par7), seasonal=list(order=c(par8,par4,par9), period=par5), include.mean=par10, method='ML')) Call: arima(x = x[1:nx], order = c(par6, par3, par7), seasonal = list(order = c(par8, par4, par9), period = par5), include.mean = par10, method = "ML") Coefficients: ar1 ar2 ma1 sar1 0.4026 0.5306 1.0000 -0.4501 s.e. 0.1575 0.1580 0.1133 0.1752 sigma^2 estimated as 141.3: log likelihood = -144.27, aic = 298.55 > (forecast <- predict(arima.out,fx)) $pred Time Series: Start = 49 End = 60 Frequency = 1 [1] 212.4672 201.9663 196.4509 213.6735 209.8459 221.8523 234.6451 230.4206 [9] 238.2045 249.0310 255.3057 235.0917 $se Time Series: Start = 49 End = 60 Frequency = 1 [1] 12.07812 20.49699 24.31372 28.08299 30.77933 33.23195 35.24140 37.03070 [9] 38.57434 39.94756 41.15927 42.24179 > (lb <- forecast$pred - 1.96 * forecast$se) Time Series: Start = 49 End = 60 Frequency = 1 [1] 188.7941 161.7922 148.7960 158.6309 149.5184 156.7176 165.5720 157.8405 [9] 162.5988 170.7338 174.6335 152.2978 > (ub <- forecast$pred + 1.96 * forecast$se) Time Series: Start = 49 End = 60 Frequency = 1 [1] 236.1403 242.1404 244.1058 268.7162 270.1734 286.9869 303.7183 303.0008 [9] 313.8102 327.3283 335.9778 317.8856 > if (par2 == 0) { + x <- exp(x) + forecast$pred <- exp(forecast$pred) + lb <- exp(lb) + ub <- exp(ub) + } > if (par2 != 0) { + x <- x^(1/par2) + forecast$pred <- forecast$pred^(1/par2) + lb <- lb^(1/par2) + ub <- ub^(1/par2) + } > (actandfor <- c(x[1:nx], forecast$pred)) [1] 96.8000 87.0000 96.3000 107.1000 115.2000 106.1000 89.5000 91.3000 [9] 97.6000 100.7000 104.6000 94.7000 101.8000 102.5000 105.3000 110.3000 [17] 109.8000 117.3000 118.8000 131.3000 125.9000 133.1000 147.0000 145.8000 [25] 164.4000 149.8000 137.7000 151.7000 156.8000 180.0000 180.4000 170.4000 [33] 191.6000 199.5000 218.2000 217.5000 205.0000 194.0000 199.3000 219.3000 [41] 211.1000 215.2000 240.2000 242.2000 240.7000 255.4000 253.0000 218.2000 [49] 212.4672 201.9663 196.4509 213.6735 209.8459 221.8523 234.6451 230.4206 [57] 238.2045 249.0310 255.3057 235.0917 > (perc.se <- (ub-forecast$pred)/1.96/forecast$pred) Time Series: Start = 49 End = 60 Frequency = 1 [1] 0.05684701 0.10148720 0.12376488 0.13142943 0.14667591 0.14979316 [7] 0.15019018 0.16070912 0.16193792 0.16041196 0.16121568 0.17968215 > postscript(file="/var/www/html/rcomp/tmp/14g0v1197046475.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mar=c(4,4,2,2),las=1) > ylim <- c( min(x[first:nx],lb), max(x[first:nx],ub)) > plot(x,ylim=ylim,type='n',xlim=c(first,lx)) > usr <- par('usr') > rect(usr[1],usr[3],nx+1,usr[4],border=NA,col='lemonchiffon') > rect(nx1,usr[3],usr[2],usr[4],border=NA,col='lavender') > abline(h= (-3:3)*2 , col ='gray', lty =3) > polygon( c(nx1:lx,lx:nx1), c(lb,rev(ub)), col = 'orange', lty=2,border=NA) > lines(nx1:lx, lb , lty=2) > lines(nx1:lx, ub , lty=2) > lines(x, lwd=2) > lines(nx1:lx, forecast$pred , lwd=2 , col ='white') > box() > par(opar) > dev.off() null device 1 > prob.dec <- array(NA, dim=fx) > prob.sdec <- array(NA, dim=fx) > prob.ldec <- array(NA, dim=fx) > prob.pval <- array(NA, dim=fx) > perf.pe <- array(0, dim=fx) > perf.mape <- array(0, dim=fx) > perf.se <- array(0, dim=fx) > perf.mse <- array(0, dim=fx) > perf.rmse <- array(0, dim=fx) > for (i in 1:fx) { + locSD <- (ub[i] - forecast$pred[i]) / 1.96 + perf.pe[i] = (x[nx+i] - forecast$pred[i]) / forecast$pred[i] + perf.mape[i] = perf.mape[i] + abs(perf.pe[i]) + perf.se[i] = (x[nx+i] - forecast$pred[i])^2 + perf.mse[i] = perf.mse[i] + perf.se[i] + prob.dec[i] = pnorm((x[nx+i-1] - forecast$pred[i]) / locSD) + prob.sdec[i] = pnorm((x[nx+i-par5] - forecast$pred[i]) / locSD) + prob.ldec[i] = pnorm((x[nx] - forecast$pred[i]) / locSD) + prob.pval[i] = pnorm(abs(x[nx+i] - forecast$pred[i]) / locSD) + } > perf.mape = perf.mape / fx > perf.mse = perf.mse / fx > perf.rmse = sqrt(perf.mse) > postscript(file="/var/www/html/rcomp/tmp/2s9361197046476.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(forecast$pred, pch=19, type='b',main='ARIMA Extrapolation Forecast', ylab='Forecast and 95% CI', xlab='time',ylim=c(min(lb),max(ub))) > dum <- forecast$pred > dum[1:12] <- x[(nx+1):lx] > lines(dum, lty=1) > lines(ub,lty=3) > lines(lb,lty=3) > dev.off() null device 1 > load(file='/var/www/html/rcomp/createtable') > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,'Univariate ARIMA Extrapolation Forecast',9,TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'time',1,header=TRUE) > a<-table.element(a,'Y[t]',1,header=TRUE) > a<-table.element(a,'F[t]',1,header=TRUE) > a<-table.element(a,'95% LB',1,header=TRUE) > a<-table.element(a,'95% UB',1,header=TRUE) > a<-table.element(a,'p-value
(H0: Y[t] = F[t])',1,header=TRUE) > a<-table.element(a,'P(F[t]>Y[t-1])',1,header=TRUE) > a<-table.element(a,'P(F[t]>Y[t-s])',1,header=TRUE) > mylab <- paste('P(F[t]>Y[',nx,sep='') > mylab <- paste(mylab,'])',sep='') > a<-table.element(a,mylab,1,header=TRUE) > a<-table.row.end(a) > for (i in (nx-par5):nx) { + a<-table.row.start(a) + a<-table.element(a,i,header=TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,'-') + a<-table.element(a,'-') + a<-table.element(a,'-') + a<-table.element(a,'-') + a<-table.element(a,'-') + a<-table.element(a,'-') + a<-table.element(a,'-') + a<-table.row.end(a) + } > for (i in 1:fx) { + a<-table.row.start(a) + a<-table.element(a,nx+i,header=TRUE) + a<-table.element(a,round(x[nx+i],4)) + a<-table.element(a,round(forecast$pred[i],4)) + a<-table.element(a,round(lb[i],4)) + a<-table.element(a,round(ub[i],4)) + a<-table.element(a,round((1-prob.pval[i]),4)) + a<-table.element(a,round((1-prob.dec[i]),4)) + a<-table.element(a,round((1-prob.sdec[i]),4)) + a<-table.element(a,round((1-prob.ldec[i]),4)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/319eu1197046476.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,'Univariate ARIMA Extrapolation Forecast Performance',7,TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'time',1,header=TRUE) > a<-table.element(a,'% S.E.',1,header=TRUE) > a<-table.element(a,'PE',1,header=TRUE) > a<-table.element(a,'MAPE',1,header=TRUE) > a<-table.element(a,'Sq.E',1,header=TRUE) > a<-table.element(a,'MSE',1,header=TRUE) > a<-table.element(a,'RMSE',1,header=TRUE) > a<-table.row.end(a) > for (i in 1:fx) { + a<-table.row.start(a) + a<-table.element(a,nx+i,header=TRUE) + a<-table.element(a,round(perc.se[i],4)) + a<-table.element(a,round(perf.pe[i],4)) + a<-table.element(a,round(perf.mape[i],4)) + a<-table.element(a,round(perf.se[i],4)) + a<-table.element(a,round(perf.mse[i],4)) + a<-table.element(a,round(perf.rmse[i],4)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/41sli1197046476.tab") > > system("convert tmp/14g0v1197046475.ps tmp/14g0v1197046475.png") > system("convert tmp/2s9361197046476.ps tmp/2s9361197046476.png") > > > proc.time() user system elapsed 1.016 0.325 1.127