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(96.8,91.2,97.1,104.9,110.9,104.8,94.1,95.8,99.3,101.1,104.0,99.0,105.4,107.1,110.7,117.1,118.7,126.5,127.5,134.6,131.8,135.9,142.7,141.7,153.4,145.0,137.7,148.3,152.2,169.4,168.6,161.1,174.1,179.0,190.6,190.0,181.6,174.8,180.5,196.8,193.8,197.0,216.3,221.4,217.9,229.7,227.4,204.2,196.6,198.8,207.5,190.7,201.6,210.5,223.5,223.8,231.2,244.0,234.7,250.2)
> par10 = 'FALSE'
> par9 = '1'
> par8 = '2'
> par7 = '2'
> par6 = '3'
> par5 = '12'
> par4 = '0'
> par3 = '1'
> par2 = '0.7'
> par1 = '36'
> #'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     ma2    sar1    sar2     sma1
      0.0318  -0.7426  -0.2177  0.2131  0.9641  0.0009  0.9987  -0.1696
s.e.  0.2297   0.3766   0.2227  0.1658  1.1443  0.0084  0.0038      NaN

sigma^2 estimated as 0.001042:  log likelihood = -25.98,  aic = 69.96
Warning messages:
1: In arima(x[1:nx], order = c(par6, par3, par7), seasonal = list(order = c(par8,  :
  possible convergence problem: optim gave code=1
2: In sqrt(diag(x$var.coef)) : NaNs produced
> (forecast <- predict(arima.out,fx))
$pred
Time Series:
Start = 25 
End = 60 
Frequency = 1 
 [1] 31.96871 30.96636 32.02248 33.38942 34.41921 33.37194 31.48956 31.79386
 [9] 32.41282 32.72860 33.23464 32.36079 33.47758 33.76984 34.38667 35.46867
[17] 35.73718 37.02495 37.18665 38.33646 37.88590 38.54598 39.62750 39.46863
[25] 39.37875 38.37805 39.43335 40.79937 41.82799 40.78323 38.90351 39.20837
[33] 39.82609 40.14202 40.64837 39.77553

$se
Time Series:
Start = 25 
End = 60 
Frequency = 1 
 [1] 0.5465469 0.5495713 0.5522928 0.5523822 0.5520420 0.5537537 0.5564834
 [8] 0.5577731 0.5577128 0.5584607 0.5606775 0.5626336 0.5635134 0.5635321
[15] 0.5643612 0.5658465 0.5668384 0.5671511 0.5676629 0.5687890 0.5699317
[22] 0.5705749 0.5710546 0.5718933 1.1062773 1.1101449 1.1134436 1.1142462
[29] 1.1148771 1.1175232 1.1209646 1.1229280 1.1237102 1.1253817 1.1284052
[36] 1.1310668

> (lb <- forecast$pred - 1.96 * forecast$se)
Time Series:
Start = 25 
End = 60 
Frequency = 1 
 [1] 30.89748 29.88920 30.93999 32.30675 33.33720 32.28658 30.39886 30.70062
 [9] 31.31971 31.63401 32.13572 31.25802 32.37309 32.66531 33.28052 34.35961
[17] 34.62618 35.91334 36.07403 37.22164 36.76884 37.42766 38.50823 38.34772
[25] 37.21045 36.20217 37.25100 38.61545 39.64283 38.59288 36.70642 37.00743
[33] 37.62362 37.93627 38.43669 37.55864
> (ub <- forecast$pred + 1.96 * forecast$se)
Time Series:
Start = 25 
End = 60 
Frequency = 1 
 [1] 33.03995 32.04352 33.10497 34.47209 35.50121 34.45730 32.58027 32.88709
 [9] 33.50594 33.82318 34.33357 33.46355 34.58206 34.87436 35.49282 36.57773
[17] 36.84819 38.13657 38.29927 39.45129 39.00297 39.66431 40.74676 40.58955
[25] 41.54706 40.55394 41.61570 42.98329 44.01315 42.97358 41.10060 41.40931
[33] 42.02857 42.34777 42.86004 41.99242
> 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$pred))
 [1]  96.8000  91.2000  97.1000 104.9000 110.9000 104.8000  94.1000  95.8000
 [9]  99.3000 101.1000 104.0000  99.0000 105.4000 107.1000 110.7000 117.1000
[17] 118.7000 126.5000 127.5000 134.6000 131.8000 135.9000 142.7000 141.7000
[25] 141.1261 134.8476 141.4653 150.1703 156.8302 150.0580 138.1141 140.0247
[33] 143.9352 145.9426 149.1769 143.6052 150.7371 152.6205 156.6185 163.7058
[41] 165.4792 174.0630 175.1500 182.9376 179.8739 184.3676 191.8017 190.7042
[49] 190.0841 183.2212 190.4607 199.9556 207.1960 199.8426 186.8153 188.9102
[57] 193.1763 195.3692 198.8992 192.8260
> (perc.se <- (ub-forecast$pred)/1.96/forecast$pred)
Time Series:
Start = 25 
End = 60 
Frequency = 1 
 [1] 0.02459756 0.02554113 0.02481596 0.02379697 0.02306597 0.02386904
 [7] 0.02543187 0.02524550 0.02475730 0.02454986 0.02427009 0.02501775
[13] 0.02421546 0.02400525 0.02360665 0.02294241 0.02280907 0.02202296
[19] 0.02194651 0.02132673 0.02162556 0.02127710 0.02071046 0.02082504
[25] 0.04060190 0.04182040 0.04081070 0.03945787 0.03849904 0.03959105
[31] 0.04165572 0.04140129 0.04078049 0.04051675 0.04011506 0.04110334
> postscript(file="/var/www/html/rcomp/tmp/1z6nj1197121798.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> opar <- par(mar=c(4,4,2,2),las=1)
> ylim <- c( min(x[first:nx],lb), max(x[first:nx],ub))
> plot(x,ylim=ylim,type='n',xlim=c(first,lx))
> usr <- par('usr')
> rect(usr[1],usr[3],nx+1,usr[4],border=NA,col='lemonchiffon')
> rect(nx1,usr[3],usr[2],usr[4],border=NA,col='lavender')
> abline(h= (-3:3)*2 , col ='gray', lty =3)
> polygon( c(nx1:lx,lx:nx1), c(lb,rev(ub)), col = 'orange', lty=2,border=NA)
> lines(nx1:lx, lb , lty=2)
> lines(nx1:lx, ub , lty=2)
> lines(x, lwd=2)
> lines(nx1:lx, forecast$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/2opgu1197121798.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/3fy3d1197121799.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/4gbzk1197121799.tab") 
> 
> system("convert tmp/1z6nj1197121798.ps tmp/1z6nj1197121798.png")
> system("convert tmp/2opgu1197121798.ps tmp/2opgu1197121798.png")
> 
> 
> proc.time()
   user  system elapsed 
  4.524   1.328   6.033