R version 2.7.0 (2008-04-22)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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> x <- c(2648.9,2669.6,3042.3,2604.2,2732.1,2621.7,2483.7,2479.3,2684.6,2834.7,2566.1,2251.2,2350,2299.8,2542.8,2530.2,2508.1,2616.8,2534.1,2181.8,2578.9,2841.9,2529.9,2103.2,2326.2,2452.6,2782.1,2727.3,2648.2,2760.7,2613,2225.4,2713.9,2923.3,2707,2473.9,2521,2531.8,3068.8,2826.9,2674.2,2966.6,2798.8,2629.6,3124.6,3115.7,3083,2863.9,2728.7,2789.4,3225.7,3148.2,2836.5,3153.5,2656.9,2834.7,3172.5,2998.8,3103.1,2735.6,2818.1,2874.4,3438.5,2949.1,3306.8,3530,3003.8,3206.4,3514.6,3522.6,3525.5,2996.2,3231.1,3030,3541.7,3113.2,3390.8,3424.2,3079.8,3123.4,3317.1,3579.9,3317.9,2668.1,3609.2,3535.2,3644.7,3925.7,3663.2,3905.3,3990,3695.8)
> par10 = 'FALSE'
> par9 = '1'
> par8 = '0'
> par7 = '0'
> par6 = '1'
> par5 = '12'
> par4 = '1'
> par3 = '1'
> par2 = '-0.1'
> 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:
          ar1     sma1
      -0.4706  -0.3439
s.e.   0.1077   0.2080

sigma^2 estimated as 6.658e-06:  log likelihood = 303.36,  aic = -600.73
> (forecast <- predict(arima.out,fx))
$pred
Time Series:
Start = 81 
End = 92 
Frequency = 1 
 [1] 0.4417954 0.4424905 0.4421674 0.4487490 0.4462900 0.4478877 0.4407006
 [8] 0.4462137 0.4430011 0.4415482 0.4469691 0.4458359

$se
Time Series:
Start = 81 
End = 92 
Frequency = 1 
 [1] 0.002580243 0.002919564 0.003503918 0.003880926 0.004275968 0.004614763
 [7] 0.004940262 0.005241186 0.005527713 0.005799220 0.006058970 0.006307853

> (lb <- forecast$pred - 1.96 * forecast$se)
Time Series:
Start = 81 
End = 92 
Frequency = 1 
 [1] 0.4367382 0.4367682 0.4352997 0.4411424 0.4379091 0.4388428 0.4310177
 [8] 0.4359410 0.4321668 0.4301817 0.4350935 0.4334725
> (ub <- forecast$pred + 1.96 * forecast$se)
Time Series:
Start = 81 
End = 92 
Frequency = 1 
 [1] 0.4468527 0.4482128 0.4490351 0.4563556 0.4546709 0.4569326 0.4503835
 [8] 0.4564864 0.4538354 0.4529147 0.4588447 0.4581993
> 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$pred))
 [1] 2648.900 2669.600 3042.300 2604.200 2732.100 2621.700 2483.700 2479.300
 [9] 2684.600 2834.700 2566.100 2251.200 2350.000 2299.800 2542.800 2530.200
[17] 2508.100 2616.800 2534.100 2181.800 2578.900 2841.900 2529.900 2103.200
[25] 2326.200 2452.600 2782.100 2727.300 2648.200 2760.700 2613.000 2225.400
[33] 2713.900 2923.300 2707.000 2473.900 2521.000 2531.800 3068.800 2826.900
[41] 2674.200 2966.600 2798.800 2629.600 3124.600 3115.700 3083.000 2863.900
[49] 2728.700 2789.400 3225.700 3148.200 2836.500 3153.500 2656.900 2834.700
[57] 3172.500 2998.800 3103.100 2735.600 2818.100 2874.400 3438.500 2949.100
[65] 3306.800 3530.000 3003.800 3206.400 3514.600 3522.600 3525.500 2996.200
[73] 3231.100 3030.000 3541.700 3113.200 3390.800 3424.200 3079.800 3123.400
[81] 3530.106 3475.045 3500.520 3019.708 3190.277 3078.284 3618.791 3195.738
[89] 3435.197 3549.924 3142.136 3222.923
> (perc.se <- (ub-forecast$pred)/1.96/forecast$pred)
Time Series:
Start = 81 
End = 92 
Frequency = 1 
 [1] 0.06225544 0.07092656 0.08645636 0.09512424 0.10649848 0.11546669
 [7] 0.12692794 0.13381096 0.14334490 0.15202084 0.15766723 0.16569011
> postscript(file="/var/www/html/rcomp/tmp/1y86y1228848619.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> opar <- par(mar=c(4,4,2,2),las=1)
> ylim <- c( min(x[first:nx],lb), max(x[first:nx],ub))
> plot(x,ylim=ylim,type='n',xlim=c(first,lx))
> usr <- par('usr')
> rect(usr[1],usr[3],nx+1,usr[4],border=NA,col='lemonchiffon')
> rect(nx1,usr[3],usr[2],usr[4],border=NA,col='lavender')
> abline(h= (-3:3)*2 , col ='gray', lty =3)
> polygon( c(nx1:lx,lx:nx1), c(lb,rev(ub)), col = 'orange', lty=2,border=NA)
> lines(nx1:lx, lb , lty=2)
> lines(nx1:lx, ub , lty=2)
> lines(x, lwd=2)
> lines(nx1:lx, forecast$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/2ok0x1228848619.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/302co1228848619.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/43d161228848619.tab") 
> 
> system("convert tmp/1y86y1228848619.ps tmp/1y86y1228848619.png")
> system("convert tmp/2ok0x1228848619.ps tmp/2ok0x1228848619.png")
> 
> 
> proc.time()
   user  system elapsed 
  1.606   0.544   1.700