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(97.7,101.5,119.6,108.1,117.8,125.5,89.2,92.3,104.6,122.8,96.0,94.6,93.3,101.1,114.2,104.7,113.3,118.2,83.6,73.9,99.5,97.7,103.0,106.3,92.2,101.8,122.8,111.8,106.3,121.5,81.9,85.4,110.9,117.3,106.3,105.5,101.3,105.9,126.3,111.9,108.9,127.2,94.2,85.7,116.2,107.2,110.6,112.0,104.5,112.0,132.8,110.8,128.7,136.8,94.9,88.8,123.2,125.3,122.7,125.7,116.3,118.7,142.0,127.9,131.9,152.3,110.8,99.1,135.0,133.2,131.0,133.9,119.9,136.9,148.9,145.1,142.4,159.6,120.7,109.0,142.0)
> par10 = 'FALSE'
> par9 = '1'
> par8 = '1'
> par7 = '1'
> par6 = '1'
> par5 = '1'
> par4 = '1'
> par3 = '0'
> par2 = '0.2'
> 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      ma1    sar1     sma1
      0.4151  -0.7933  0.4151  -0.7933
s.e.  0.2279   0.1342  0.2279   0.1342

sigma^2 estimated as 0.004274:  log likelihood = 88.22,  aic = -166.44
> (forecast <- predict(arima.out,fx))
$pred
Time Series:
Start = 70 
End = 81 
Frequency = 1 
 [1] 2.628560 2.618372 2.616584 2.616855 2.617387 2.617783 2.618020 2.618148
 [9] 2.618214 2.618246 2.618262 2.618269

$se
Time Series:
Start = 70 
End = 81 
Frequency = 1 
 [1] 0.06537747 0.06728861 0.06745586 0.06757379 0.06778035 0.06809083
 [7] 0.06847824 0.06891117 0.06936734 0.06983374 0.07030350 0.07077321

> (lb <- forecast$pred - 1.96 * forecast$se)
Time Series:
Start = 70 
End = 81 
Frequency = 1 
 [1] 2.500420 2.486487 2.484370 2.484410 2.484538 2.484325 2.483803 2.483082
 [9] 2.482254 2.481372 2.480467 2.479554
> (ub <- forecast$pred + 1.96 * forecast$se)
Time Series:
Start = 70 
End = 81 
Frequency = 1 
 [1] 2.756700 2.750258 2.748797 2.749299 2.750237 2.751241 2.752237 2.753214
 [9] 2.754174 2.755121 2.756057 2.756985
> 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$pred))
 [1]  97.7000 101.5000 119.6000 108.1000 117.8000 125.5000  89.2000  92.3000
 [9] 104.6000 122.8000  96.0000  94.6000  93.3000 101.1000 114.2000 104.7000
[17] 113.3000 118.2000  83.6000  73.9000  99.5000  97.7000 103.0000 106.3000
[25]  92.2000 101.8000 122.8000 111.8000 106.3000 121.5000  81.9000  85.4000
[33] 110.9000 117.3000 106.3000 105.5000 101.3000 105.9000 126.3000 111.9000
[41] 108.9000 127.2000  94.2000  85.7000 116.2000 107.2000 110.6000 112.0000
[49] 104.5000 112.0000 132.8000 110.8000 128.7000 136.8000  94.9000  88.8000
[57] 123.2000 125.3000 122.7000 125.7000 116.3000 118.7000 142.0000 127.9000
[65] 131.9000 152.3000 110.8000  99.1000 135.0000 125.4844 123.0714 122.6516
[73] 122.7151 122.8401 122.9330 122.9886 123.0187 123.0342 123.0418 123.0455
[81] 123.0472
> (perc.se <- (ub-forecast$pred)/1.96/forecast$pred)
Time Series:
Start = 70 
End = 81 
Frequency = 1 
 [1] 0.1370903 0.1421060 0.1426021 0.1428603 0.1433092 0.1440085 0.1448974
 [8] 0.1459000 0.1469619 0.1480512 0.1491509 0.1502524
> postscript(file="/var/www/html/rcomp/tmp/1jzog1229519623.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> opar <- par(mar=c(4,4,2,2),las=1)
> ylim <- c( min(x[first:nx],lb), max(x[first:nx],ub))
> plot(x,ylim=ylim,type='n',xlim=c(first,lx))
> usr <- par('usr')
> rect(usr[1],usr[3],nx+1,usr[4],border=NA,col='lemonchiffon')
> rect(nx1,usr[3],usr[2],usr[4],border=NA,col='lavender')
> abline(h= (-3:3)*2 , col ='gray', lty =3)
> polygon( c(nx1:lx,lx:nx1), c(lb,rev(ub)), col = 'orange', lty=2,border=NA)
> lines(nx1:lx, lb , lty=2)
> lines(nx1:lx, ub , lty=2)
> lines(x, lwd=2)
> lines(nx1:lx, forecast$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/2yvkx1229519623.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/32lhj1229519623.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/4w1931229519624.tab") 
> 
> system("convert tmp/1jzog1229519623.ps tmp/1jzog1229519623.png")
> system("convert tmp/2yvkx1229519623.ps tmp/2yvkx1229519623.png")
> 
> 
> proc.time()
   user  system elapsed 
  0.587   0.319   0.703