R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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> x <- c(93.7,105.7,109.5,105.3,102.8,100.6,97.6,110.3,107.2,107.2,108.1,97.1,92.2,112.2,111.6,115.7,111.3,104.2,103.2,112.7,106.4,102.6,110.6,95.2,89,112.5,116.8,107.2,113.6,101.8,102.6,122.7,110.3,110.5,121.6,100.3,100.7,123.4,127.1,124.1,131.2,111.6,114.2,130.1,125.9,119,133.8,107.5,113.5,134.4,126.8,135.6,139.9,129.8,131,153.1,134.1,144.1,155.9,123.3,128.1,144.3,153,149.9,150.9,141,138.9,157.4,142.9,151.7,161,138.5,135.9,151.5,164,159.1,157,142.1,144.8,152.1,154.6,148.7,157.7,146.4,136.5)
> par10 = 'FALSE'
> par9 = '0'
> par8 = '0'
> par7 = '2'
> par6 = '0'
> par5 = '12'
> par4 = '1'
> par3 = '1'
> par2 = '0.2'
> par1 = '0'
> #'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:
          ma1     ma2
      -0.7954  0.2878
s.e.   0.1113  0.1184

sigma^2 estimated as 0.0005097:  log likelihood = 170.45,  aic = -334.9
> (forecast <- predict(arima.out,fx))
$pred
Time Series:
Start = 86 
End = 97 
Frequency = 1 
 [1] 2.738971 2.782859 2.766086 2.758771 2.704490 2.714654 2.741393 2.750314
 [9] 2.729070 2.761218 2.720605 2.682908

$se
Time Series:
Start = 86 
End = 97 
Frequency = 1 
 [1] 0.02257591 0.02304342 0.02558394 0.02789403 0.03002691 0.03201803
 [7] 0.03389237 0.03566835 0.03736001 0.03897831 0.04053206 0.04202840

> (lb <- forecast$pred - 1.96 * forecast$se)
Time Series:
Start = 86 
End = 97 
Frequency = 1 
 [1] 2.694723 2.737694 2.715941 2.704099 2.645637 2.651898 2.674964 2.680404
 [9] 2.655844 2.684820 2.641162 2.600532
> (ub <- forecast$pred + 1.96 * forecast$se)
Time Series:
Start = 86 
End = 97 
Frequency = 1 
 [1] 2.783220 2.828024 2.816230 2.813443 2.763343 2.777409 2.807822 2.820224
 [9] 2.802295 2.837615 2.800048 2.765283
> 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)
+ }
> if (par2 < 0) {
+ olb <- lb
+ lb <- ub
+ ub <- olb
+ }
> (actandfor <- c(x[1:nx], forecast$pred))
 [1]  93.7000 105.7000 109.5000 105.3000 102.8000 100.6000  97.6000 110.3000
 [9] 107.2000 107.2000 108.1000  97.1000  92.2000 112.2000 111.6000 115.7000
[17] 111.3000 104.2000 103.2000 112.7000 106.4000 102.6000 110.6000  95.2000
[25]  89.0000 112.5000 116.8000 107.2000 113.6000 101.8000 102.6000 122.7000
[33] 110.3000 110.5000 121.6000 100.3000 100.7000 123.4000 127.1000 124.1000
[41] 131.2000 111.6000 114.2000 130.1000 125.9000 119.0000 133.8000 107.5000
[49] 113.5000 134.4000 126.8000 135.6000 139.9000 129.8000 131.0000 153.1000
[57] 134.1000 144.1000 155.9000 123.3000 128.1000 144.3000 153.0000 149.9000
[65] 150.9000 141.0000 138.9000 157.4000 142.9000 151.7000 161.0000 138.5000
[73] 135.9000 151.5000 164.0000 159.1000 157.0000 142.1000 144.8000 152.1000
[81] 154.6000 148.7000 157.7000 146.4000 136.5000 154.1479 166.8998 161.9304
[89] 159.8005 144.6861 147.4253 154.8305 157.3663 151.3816 160.5105 149.0484
[97] 139.0044
> (perc.se <- (ub-forecast$pred)/1.96/forecast$pred)
Time Series:
Start = 86 
End = 97 
Frequency = 1 
 [1] 0.04256567 0.04276832 0.04795313 0.05259904 0.05798228 0.06176292
 [7] 0.06488528 0.06822554 0.07222130 0.07459703 0.07897012 0.08328601
> postscript(file="/var/www/html/rcomp/tmp/1ora51229261285.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> opar <- par(mar=c(4,4,2,2),las=1)
> ylim <- c( min(x[first:nx],lb), max(x[first:nx],ub))
> plot(x,ylim=ylim,type='n',xlim=c(first,lx))
> usr <- par('usr')
> rect(usr[1],usr[3],nx+1,usr[4],border=NA,col='lemonchiffon')
> rect(nx1,usr[3],usr[2],usr[4],border=NA,col='lavender')
> abline(h= (-3:3)*2 , col ='gray', lty =3)
> polygon( c(nx1:lx,lx:nx1), c(lb,rev(ub)), col = 'orange', lty=2,border=NA)
> lines(nx1:lx, lb , lty=2)
> lines(nx1:lx, ub , lty=2)
> lines(x, lwd=2)
> lines(nx1:lx, forecast$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/29t3k1229261285.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 
> 
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> 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<br />(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/37nun1229261285.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/474qb1229261285.tab") 
> 
> system("convert tmp/1ora51229261285.ps tmp/1ora51229261285.png")
> system("convert tmp/29t3k1229261285.ps tmp/29t3k1229261285.png")
> 
> 
> proc.time()
   user  system elapsed 
  0.595   0.330   0.746