R version 2.6.0 (2007-10-03)
Copyright (C) 2007 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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> x <- c(105.3,103,103.8,103.4,105.8,101.4,97,94.3,96.6,97.1,95.7,96.9,97.4,95.3,93.6,91.5,93.1,91.7,94.3,93.9,90.9,88.3,91.3,91.7,92.4,92,95.6,95.8,96.4,99,107,109.7,116.2,115.9,113.8,112.6,113.7,115.9,110.3,111.3,113.4,108.2,104.8,106,110.9,115,118.4,121.4,128.8,131.7,141.7,142.9,139.4,134.7,125,113.6,111.5,108.5,112.3,116.6,115.5,120.1,132.9,128.1,129.3,132.5,131,124.9,123.45,122,122.1,127.4,135.2,137.3,135,136,138.4,134.7,138.4,133.9,133.6,141.2,148.9,148.9,156.6,161.6,160.7,156,159.5,168.7,169.9,169.9,169.9)
> par10 = 'TRUE'
> par9 = '1'
> par8 = '1'
> par7 = '1'
> par6 = '0'
> par5 = '12'
> par4 = '0'
> par3 = '1'
> par2 = '1'
> 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:
         ma1     sar1    sma1
      0.2755  -0.7730  0.9872
s.e.  0.1054   0.3058  2.1281

sigma^2 estimated as 13.10:  log likelihood = -219.67,  aic = 447.34
> (forecast <- predict(arima.out,fx))
$pred
Time Series:
Start = 82 
End = 93 
Frequency = 1 
 [1] 134.5786 135.0690 135.7317 137.8389 137.6153 137.8722 138.9261 138.5374
 [9] 137.5491 137.7640 136.4434 136.8173

$se
Time Series:
Start = 82 
End = 93 
Frequency = 1 
 [1]  3.725698  6.038373  7.683949  9.034348 10.202888 11.250707 12.208927
 [8] 13.097228 13.928993 14.713814 15.458842 16.169579

> (lb <- forecast$pred - 1.96 * forecast$se)
Time Series:
Start = 82 
End = 93 
Frequency = 1 
 [1] 127.2762 123.2338 120.6711 120.1315 117.6176 115.8208 114.9967 112.8668
 [9] 110.2483 108.9249 106.1441 105.1249
> (ub <- forecast$pred + 1.96 * forecast$se)
Time Series:
Start = 82 
End = 93 
Frequency = 1 
 [1] 141.8810 146.9042 150.7922 155.5462 157.6130 159.9236 162.8556 164.2080
 [9] 164.8499 166.6031 166.7427 168.5097
> 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$pred))
 [1] 105.3000 103.0000 103.8000 103.4000 105.8000 101.4000  97.0000  94.3000
 [9]  96.6000  97.1000  95.7000  96.9000  97.4000  95.3000  93.6000  91.5000
[17]  93.1000  91.7000  94.3000  93.9000  90.9000  88.3000  91.3000  91.7000
[25]  92.4000  92.0000  95.6000  95.8000  96.4000  99.0000 107.0000 109.7000
[33] 116.2000 115.9000 113.8000 112.6000 113.7000 115.9000 110.3000 111.3000
[41] 113.4000 108.2000 104.8000 106.0000 110.9000 115.0000 118.4000 121.4000
[49] 128.8000 131.7000 141.7000 142.9000 139.4000 134.7000 125.0000 113.6000
[57] 111.5000 108.5000 112.3000 116.6000 115.5000 120.1000 132.9000 128.1000
[65] 129.3000 132.5000 131.0000 124.9000 123.4500 122.0000 122.1000 127.4000
[73] 135.2000 137.3000 135.0000 136.0000 138.4000 134.7000 138.4000 133.9000
[81] 133.6000 134.5786 135.0690 135.7317 137.8389 137.6153 137.8722 138.9261
[89] 138.5374 137.5491 137.7640 136.4434 136.8173
> (perc.se <- (ub-forecast$pred)/1.96/forecast$pred)
Time Series:
Start = 82 
End = 93 
Frequency = 1 
 [1] 0.02768418 0.04470583 0.05661132 0.06554283 0.07414065 0.08160243
 [7] 0.08788070 0.09453929 0.10126560 0.10680451 0.11329856 0.11818374
> postscript(file="/var/www/html/rcomp/tmp/14f3l1197037998.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> opar <- par(mar=c(4,4,2,2),las=1)
> ylim <- c( min(x[first:nx],lb), max(x[first:nx],ub))
> plot(x,ylim=ylim,type='n',xlim=c(first,lx))
> usr <- par('usr')
> rect(usr[1],usr[3],nx+1,usr[4],border=NA,col='lemonchiffon')
> rect(nx1,usr[3],usr[2],usr[4],border=NA,col='lavender')
> abline(h= (-3:3)*2 , col ='gray', lty =3)
> polygon( c(nx1:lx,lx:nx1), c(lb,rev(ub)), col = 'orange', lty=2,border=NA)
> lines(nx1:lx, lb , lty=2)
> lines(nx1:lx, ub , lty=2)
> lines(x, lwd=2)
> lines(nx1:lx, forecast$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/20nx31197037998.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<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/3syx51197037998.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/42m1o1197037999.tab") 
> 
> system("convert tmp/14f3l1197037998.ps tmp/14f3l1197037998.png")
> system("convert tmp/20nx31197037998.ps tmp/20nx31197037998.png")
> 
> 
> proc.time()
   user  system elapsed 
  0.995   0.313   1.128