R version 2.6.1 (2007-11-26)
Copyright (C) 2007 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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> x <- c(153.4,159.5,157.4,169.1,172.6,161.7,159.2,157.4,153.9,144.8,142.2,140.1,143.4,153.3,166.9,170.6,182.8,170.3,156.6,155.2,154.7,151.6,152.1,153.2,149.5,149.7,144.3,140,137.8,132.2,128.9,123.1,120.4,122.8,126,124.5,120.6,114.7,111.7,109.1,108,107.7,99.9,103.7,103.4,103.4,104.7,105.8,105.3,103,103.8,103.4,105.8,101.4,97,94.3,96.6,97.1,95.7,96.9,97.4,95.3,93.6,91.5,93.1,91.7,94.3,93.9,90.9,88.3,91.3,91.7,92.4,92,95.6,95.8,96.4,99,107,109.7,116.2,115.9,113.8,112.6,113.7,115.9,110.3,111.3,113.4,108.2,104.8,106,110.9,115,118.4,121.4,128.8,131.7,141.7,142.9,139.4,134.7,125,113.6,111.5,108.5,112.3,116.6,115.5,120.1,132.9,128.1,129.3,132.5,131,124.9,120.8,122,122.1,127.4,135.2,137.3,135,136,138.4,134.7,138.4,133.9,133.6)
> par10 = 'FALSE'
> par9 = '1'
> par8 = '2'
> par7 = '1'
> par6 = '3'
> par5 = '12'
> par4 = '0'
> par3 = '1'
> par2 = '0.0'
> 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     ar3     ma1     sar1     sar2    sma1
      -0.0740  0.1105  0.1188  0.3381  -0.4246  -0.0736  0.5684
s.e.   0.4011  0.1425  0.0995  0.3915   0.6367   0.1848  0.6384

sigma^2 estimated as 0.001117:  log likelihood = 229.07,  aic = -442.14
> (forecast <- predict(arima.out,fx))
$pred
Time Series:
Start = 118 
End = 129 
Frequency = 1 
 [1] 4.775667 4.765815 4.761849 4.750235 4.752564 4.748417 4.741550 4.748167
 [9] 4.751781 4.755732 4.764355 4.762339

$se
Time Series:
Start = 118 
End = 129 
Frequency = 1 
 [1] 0.03342850 0.05388190 0.07039402 0.08635671 0.10032114 0.11293442
 [7] 0.12453225 0.13520317 0.14514645 0.15447840 0.16328777 0.17165320

> (lb <- forecast$pred - 1.96 * forecast$se)
Time Series:
Start = 118 
End = 129 
Frequency = 1 
 [1] 4.710148 4.660207 4.623877 4.580975 4.555935 4.527066 4.497467 4.483169
 [9] 4.467293 4.452954 4.444311 4.425899
> (ub <- forecast$pred + 1.96 * forecast$se)
Time Series:
Start = 118 
End = 129 
Frequency = 1 
 [1] 4.841187 4.871424 4.899821 4.919494 4.949194 4.969769 4.985633 5.013166
 [9] 5.036268 5.058510 5.084400 5.098780
> 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$pred))
  [1] 153.4000 159.5000 157.4000 169.1000 172.6000 161.7000 159.2000 157.4000
  [9] 153.9000 144.8000 142.2000 140.1000 143.4000 153.3000 166.9000 170.6000
 [17] 182.8000 170.3000 156.6000 155.2000 154.7000 151.6000 152.1000 153.2000
 [25] 149.5000 149.7000 144.3000 140.0000 137.8000 132.2000 128.9000 123.1000
 [33] 120.4000 122.8000 126.0000 124.5000 120.6000 114.7000 111.7000 109.1000
 [41] 108.0000 107.7000  99.9000 103.7000 103.4000 103.4000 104.7000 105.8000
 [49] 105.3000 103.0000 103.8000 103.4000 105.8000 101.4000  97.0000  94.3000
 [57]  96.6000  97.1000  95.7000  96.9000  97.4000  95.3000  93.6000  91.5000
 [65]  93.1000  91.7000  94.3000  93.9000  90.9000  88.3000  91.3000  91.7000
 [73]  92.4000  92.0000  95.6000  95.8000  96.4000  99.0000 107.0000 109.7000
 [81] 116.2000 115.9000 113.8000 112.6000 113.7000 115.9000 110.3000 111.3000
 [89] 113.4000 108.2000 104.8000 106.0000 110.9000 115.0000 118.4000 121.4000
 [97] 128.8000 131.7000 141.7000 142.9000 139.4000 134.7000 125.0000 113.6000
[105] 111.5000 108.5000 112.3000 116.6000 115.5000 120.1000 132.9000 128.1000
[113] 129.3000 132.5000 131.0000 124.9000 120.8000 118.5894 117.4268 116.9620
[121] 115.6114 115.8811 115.4015 114.6117 115.3726 115.7903 116.2487 117.2555
[129] 117.0194
> (perc.se <- (ub-forecast$pred)/1.96/forecast$pred)
Time Series:
Start = 118 
End = 129 
Frequency = 1 
 [1] 0.03454792 0.05682995 0.07548149 0.09409543 0.11086371 0.12640912
 [7] 0.14104620 0.15481054 0.16789800 0.18041503 0.19244306 0.20405880
> postscript(file="/var/www/html/rcomp/tmp/1cgxf1197381291.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> opar <- par(mar=c(4,4,2,2),las=1)
> ylim <- c( min(x[first:nx],lb), max(x[first:nx],ub))
> plot(x,ylim=ylim,type='n',xlim=c(first,lx))
> usr <- par('usr')
> rect(usr[1],usr[3],nx+1,usr[4],border=NA,col='lemonchiffon')
> rect(nx1,usr[3],usr[2],usr[4],border=NA,col='lavender')
> abline(h= (-3:3)*2 , col ='gray', lty =3)
> polygon( c(nx1:lx,lx:nx1), c(lb,rev(ub)), col = 'orange', lty=2,border=NA)
> lines(nx1:lx, lb , lty=2)
> lines(nx1:lx, ub , lty=2)
> lines(x, lwd=2)
> lines(nx1:lx, forecast$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/2bsb01197381291.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/358jf1197381291.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/4wpyp1197381291.tab") 
> 
> system("convert tmp/1cgxf1197381291.ps tmp/1cgxf1197381291.png")
> system("convert tmp/2bsb01197381291.ps tmp/2bsb01197381291.png")
> 
> 
> proc.time()
   user  system elapsed 
  5.717   1.199   6.333