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(99.8,96.8,87.0,96.3,107.1,115.2,106.1,89.5,91.3,97.6,100.7,104.6,94.7,101.8,102.5,105.3,110.3,109.8,117.3,118.8,131.3,125.9,133.1,147.0,145.8,164.4,149.8,137.7,151.7,156.8,180.0,180.4,170.4,191.6,199.5,218.2,217.5,205.0,194.0,199.3,219.3,211.1,215.2,240.2,242.2,240.7,255.4,253.0,218.2,203.7,205.6,215.6,188.5,202.9,214.0,230.3,230.0,241.0,259.6,247.8,270.3)
> par10 = 'FALSE'
> par9 = '0'
> par8 = '1'
> par7 = '1'
> par6 = '3'
> par5 = '1'
> par4 = '0'
> par3 = '1'
> par2 = '0.0'
> par1 = '20'
> #'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
      0.3020  -0.2296  -0.1841  0.0866  -0.3782
s.e.  0.3986   0.2452   0.1993  0.3792   0.3498

sigma^2 estimated as 0.005315:  log likelihood = 47.86,  aic = -83.72
> (forecast <- predict(arima.out,fx))
$pred
Time Series:
Start = 42 
End = 61 
Frequency = 1 
 [1] 5.405425 5.389788 5.361464 5.354709 5.361685 5.370696 5.373006 5.370371
 [9] 5.367378 5.366657 5.367610 5.368615 5.368832 5.368492 5.368154 5.368090
[17] 5.368211 5.368325 5.368343 5.368300

$se
Time Series:
Start = 42 
End = 61 
Frequency = 1 
 [1] 0.07290216 0.10363396 0.12243194 0.13078646 0.13772219 0.14579039
 [7] 0.15532737 0.16468297 0.17297000 0.18033753 0.18731967 0.19422357
[13] 0.20105121 0.20768027 0.21404747 0.22018146 0.22614263 0.23196900
[19] 0.23766781 0.24323473

> (lb <- forecast$pred - 1.96 * forecast$se)
Time Series:
Start = 42 
End = 61 
Frequency = 1 
 [1] 5.262537 5.186665 5.121498 5.098367 5.091750 5.084946 5.068565 5.047592
 [9] 5.028357 5.013195 5.000464 4.987937 4.974772 4.961438 4.948621 4.936535
[17] 4.924972 4.913665 4.902514 4.891560
> (ub <- forecast$pred + 1.96 * forecast$se)
Time Series:
Start = 42 
End = 61 
Frequency = 1 
 [1] 5.548313 5.592910 5.601431 5.611050 5.631621 5.656445 5.677448 5.693149
 [9] 5.706399 5.720118 5.734757 5.749293 5.762893 5.775545 5.787687 5.799646
[17] 5.811451 5.822984 5.834172 5.845040
> 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$pred))
 [1]  99.8000  96.8000  87.0000  96.3000 107.1000 115.2000 106.1000  89.5000
 [9]  91.3000  97.6000 100.7000 104.6000  94.7000 101.8000 102.5000 105.3000
[17] 110.3000 109.8000 117.3000 118.8000 131.3000 125.9000 133.1000 147.0000
[25] 145.8000 164.4000 149.8000 137.7000 151.7000 156.8000 180.0000 180.4000
[33] 170.4000 191.6000 199.5000 218.2000 217.5000 205.0000 194.0000 199.3000
[41] 219.3000 222.6108 219.1569 213.0366 211.6024 213.0837 215.0124 215.5098
[49] 214.9426 214.3002 214.1457 214.3500 214.5655 214.6121 214.5390 214.4666
[57] 214.4529 214.4789 214.5032 214.5071 214.4979
> (perc.se <- (ub-forecast$pred)/1.96/forecast$pred)
Time Series:
Start = 42 
End = 61 
Frequency = 1 
 [1] 0.07836778 0.11490951 0.13837076 0.14907851 0.15810202 0.16875440
 [7] 0.18156517 0.19436714 0.20590465 0.21632053 0.22633136 0.23636563
[13] 0.24642351 0.25631848 0.26594440 0.27533207 0.28456400 0.29369203
[19] 0.30272162 0.31164014
> postscript(file="/var/www/html/rcomp/tmp/10ymw1197843178.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> opar <- par(mar=c(4,4,2,2),las=1)
> ylim <- c( min(x[first:nx],lb), max(x[first:nx],ub))
> plot(x,ylim=ylim,type='n',xlim=c(first,lx))
> usr <- par('usr')
> rect(usr[1],usr[3],nx+1,usr[4],border=NA,col='lemonchiffon')
> rect(nx1,usr[3],usr[2],usr[4],border=NA,col='lavender')
> abline(h= (-3:3)*2 , col ='gray', lty =3)
> polygon( c(nx1:lx,lx:nx1), c(lb,rev(ub)), col = 'orange', lty=2,border=NA)
> lines(nx1:lx, lb , lty=2)
> lines(nx1:lx, ub , lty=2)
> lines(x, lwd=2)
> lines(nx1:lx, forecast$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/2xcvs1197843178.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]
Warning message:
In NextMethod("[<-") :
  number of items to replace is not a multiple of replacement length
> 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/3iuj81197843179.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/4va4l1197843179.tab") 
> 
> system("convert tmp/10ymw1197843178.ps tmp/10ymw1197843178.png")
> system("convert tmp/2xcvs1197843178.ps tmp/2xcvs1197843178.png")
> 
> 
> proc.time()
   user  system elapsed 
  0.881   0.311   1.077