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(102.7,103.2,105.6,103.9,107.2,100.7,92.1,90.3,93.4,98.5,100.8,102.3,104.7,101.1,101.4,99.5,98.4,96.3,100.7,101.2,100.3,97.8,97.4,98.6,99.7,99,98.1,97,98.5,103.8,114.4,124.5,134.2,131.8,125.6,119.9,114.9,115.5,112.5,111.4,115.3,110.8,103.7,111.1,113,111.2,117.6,121.7,127.3,129.8,137.1,141.4,137.4,130.7,117.2,110.8,111.4,108.2,108.8,110.2,109.5,109.5,116,111.2,112.1,114,119.1,114.1,115.1,115.4,110.8,116,119.2,126.5,127.8,131.3,140.3,137.3,143,134.5,139.9,159.3,170.4,175,175.8,180.9,180.3,169.6,172.3,184.8,177.7,184.6,211.4)
> par10 = 'FALSE'
> par9 = '0'
> par8 = '2'
> 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     sar2
      0.2315  0.0481  -0.3264
s.e.  0.1022  0.1265   0.1137

sigma^2 estimated as 18.33:  log likelihood = -231.25,  aic = 470.5
> (forecast <- predict(arima.out,fx))
$pred
Time Series:
Start = 82 
End = 93 
Frequency = 1 
 [1] 142.8308 142.4135 142.2069 142.5894 142.9408 140.8819 142.6170 142.7565
 [9] 141.9920 140.6018 141.8246 141.7581

$se
Time Series:
Start = 82 
End = 93 
Frequency = 1 
 [1]  4.281819  6.792745  8.599349 10.087428 11.382601 12.544761 13.608028
 [8] 14.594035 15.517514 16.389041 17.216506 18.005985

> (lb <- forecast$pred - 1.96 * forecast$se)
Time Series:
Start = 82 
End = 93 
Frequency = 1 
 [1] 134.4384 129.0997 125.3522 122.8181 120.6309 116.2942 115.9453 114.1522
 [9] 111.5777 108.4793 108.0802 106.4664
> (ub <- forecast$pred + 1.96 * forecast$se)
Time Series:
Start = 82 
End = 93 
Frequency = 1 
 [1] 151.2232 155.7273 159.0616 162.3608 165.2507 165.4697 169.2888 171.3608
 [9] 172.4063 172.7244 175.5689 177.0499
> 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$pred))
 [1] 102.7000 103.2000 105.6000 103.9000 107.2000 100.7000  92.1000  90.3000
 [9]  93.4000  98.5000 100.8000 102.3000 104.7000 101.1000 101.4000  99.5000
[17]  98.4000  96.3000 100.7000 101.2000 100.3000  97.8000  97.4000  98.6000
[25]  99.7000  99.0000  98.1000  97.0000  98.5000 103.8000 114.4000 124.5000
[33] 134.2000 131.8000 125.6000 119.9000 114.9000 115.5000 112.5000 111.4000
[41] 115.3000 110.8000 103.7000 111.1000 113.0000 111.2000 117.6000 121.7000
[49] 127.3000 129.8000 137.1000 141.4000 137.4000 130.7000 117.2000 110.8000
[57] 111.4000 108.2000 108.8000 110.2000 109.5000 109.5000 116.0000 111.2000
[65] 112.1000 114.0000 119.1000 114.1000 115.1000 115.4000 110.8000 116.0000
[73] 119.2000 126.5000 127.8000 131.3000 140.3000 137.3000 143.0000 134.5000
[81] 139.9000 142.8308 142.4135 142.2069 142.5894 142.9408 140.8819 142.6170
[89] 142.7565 141.9920 140.6018 141.8246 141.7581
> (perc.se <- (ub-forecast$pred)/1.96/forecast$pred)
Time Series:
Start = 82 
End = 93 
Frequency = 1 
 [1] 0.02997826 0.04769733 0.06047068 0.07074458 0.07963157 0.08904450
 [7] 0.09541657 0.10223025 0.10928443 0.11656349 0.12139297 0.12701906
> postscript(file="/var/www/html/rcomp/tmp/15bkn1197392572.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> opar <- par(mar=c(4,4,2,2),las=1)
> ylim <- c( min(x[first:nx],lb), max(x[first:nx],ub))
> plot(x,ylim=ylim,type='n',xlim=c(first,lx))
> usr <- par('usr')
> rect(usr[1],usr[3],nx+1,usr[4],border=NA,col='lemonchiffon')
> rect(nx1,usr[3],usr[2],usr[4],border=NA,col='lavender')
> abline(h= (-3:3)*2 , col ='gray', lty =3)
> polygon( c(nx1:lx,lx:nx1), c(lb,rev(ub)), col = 'orange', lty=2,border=NA)
> lines(nx1:lx, lb , lty=2)
> lines(nx1:lx, ub , lty=2)
> lines(x, lwd=2)
> lines(nx1:lx, forecast$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/28ouq1197392573.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/39eng1197392573.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/4nd3x1197392573.tab") 
> 
> system("convert tmp/15bkn1197392572.ps tmp/15bkn1197392572.png")
> system("convert tmp/28ouq1197392573.ps tmp/28ouq1197392573.png")
> 
> 
> proc.time()
   user  system elapsed 
  0.969   0.331   1.213