R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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> x <- c(44164,40399,36763,37903,35532,35533,32110,33374,35462,33508,36080,34560,38737,38144,37594,36424,36843,37246,38661,40454,44928,48441,48140,45998,47369,49554,47510,44873,45344,42413,36912,43452,42142,44382,43636,44167,44423,42868,43908,42013,38846,35087,33026,34646,37135,37985,43121,43722,43630,42234,39351,39327,35704,30466,28155,29257,29998,32529,34787,33855,34556,31348,30805,28353,24514,21106,21346,23335,24379,26290,30084,29429,30632,27349,27264,27474,24482,21453,18788,19282,19713,21917,23812,23785,24696,24562,23580,24939,23899,21454,19761,19815,20780,23462,25005,24725,26198,27543,26471,26558,25317,22896)
> par10 = 'FALSE'
> par9 = '1'
> par8 = '0'
> par7 = '0'
> par6 = '0'
> par5 = '12'
> par4 = '1'
> 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: Wessa P., (2009), ARIMA Forecasting (v1.0.5) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_arimaforecasting.wasp/
> #Source of accompanying publication: 
> #Technical description: 
> 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:
         sma1
      -0.9998
s.e.   0.5609

sigma^2 estimated as 3204456:  log likelihood = -697.99,  aic = 1399.98
> (forecast <- predict(arima.out,par1))
$pred
Time Series:
Start = 91 
End = 102 
Frequency = 1 
 [1] 19410.33 21524.95 22947.43 24561.06 26647.98 26056.06 27274.28 25805.67
 [9] 24595.31 23911.71 21893.99 19343.27

$se
Time Series:
Start = 91 
End = 102 
Frequency = 1 
 [1] 1913.508 2706.108 3314.292 3827.014 4278.732 4687.115 5062.663 5406.926
 [9] 5730.545 6036.841 6328.329 6606.969

> (lb <- forecast$pred - 1.96 * forecast$se)
Time Series:
Start = 91 
End = 102 
Frequency = 1 
 [1] 15659.854 16220.976 16451.418 17060.113 18261.670 16869.310 17351.464
 [8] 15208.094 13363.442 12079.501  9490.463  6393.610
> (ub <- forecast$pred + 1.96 * forecast$se)
Time Series:
Start = 91 
End = 102 
Frequency = 1 
 [1] 23160.80 26828.92 29443.44 32062.01 35034.30 35242.80 37197.10 36403.25
 [9] 35827.18 35743.92 34297.51 32292.93
> 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$pred))
  [1] 44164.00 40399.00 36763.00 37903.00 35532.00 35533.00 32110.00 33374.00
  [9] 35462.00 33508.00 36080.00 34560.00 38737.00 38144.00 37594.00 36424.00
 [17] 36843.00 37246.00 38661.00 40454.00 44928.00 48441.00 48140.00 45998.00
 [25] 47369.00 49554.00 47510.00 44873.00 45344.00 42413.00 36912.00 43452.00
 [33] 42142.00 44382.00 43636.00 44167.00 44423.00 42868.00 43908.00 42013.00
 [41] 38846.00 35087.00 33026.00 34646.00 37135.00 37985.00 43121.00 43722.00
 [49] 43630.00 42234.00 39351.00 39327.00 35704.00 30466.00 28155.00 29257.00
 [57] 29998.00 32529.00 34787.00 33855.00 34556.00 31348.00 30805.00 28353.00
 [65] 24514.00 21106.00 21346.00 23335.00 24379.00 26290.00 30084.00 29429.00
 [73] 30632.00 27349.00 27264.00 27474.00 24482.00 21453.00 18788.00 19282.00
 [81] 19713.00 21917.00 23812.00 23785.00 24696.00 24562.00 23580.00 24939.00
 [89] 23899.00 21454.00 19410.33 21524.95 22947.43 24561.06 26647.98 26056.06
 [97] 27274.28 25805.67 24595.31 23911.71 21893.99 19343.27
> (perc.se <- (ub-forecast$pred)/1.96/forecast$pred)
Time Series:
Start = 91 
End = 102 
Frequency = 1 
 [1] 0.09858193 0.12571962 0.14442976 0.15581633 0.16056493 0.17988584
 [7] 0.18562038 0.20952475 0.23299341 0.25246380 0.28904414 0.34156423
> postscript(file="/var/www/html/rcomp/tmp/18fei1293200722.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> opar <- par(mar=c(4,4,2,2),las=1)
> ylim <- c( min(x[first:nx],lb), max(x[first:nx],ub))
> plot(x,ylim=ylim,type='n',xlim=c(first,lx))
> usr <- par('usr')
> rect(usr[1],usr[3],nx+1,usr[4],border=NA,col='lemonchiffon')
> rect(nx1,usr[3],usr[2],usr[4],border=NA,col='lavender')
> abline(h= (-3:3)*2 , col ='gray', lty =3)
> polygon( c(nx1:lx,lx:nx1), c(lb,rev(ub)), col = 'orange', lty=2,border=NA)
> lines(nx1:lx, lb , lty=2)
> lines(nx1:lx, ub , lty=2)
> lines(x, lwd=2)
> lines(nx1:lx, forecast$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.mape1 <- array(0, dim=fx)
> perf.se <- array(0, dim=fx)
> perf.mse <- array(0, dim=fx)
> perf.mse1 <- 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$pred[i]
+ perf.se[i] = (x[nx+i] - forecast$pred[i])^2
+ 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[1] = abs(perf.pe[1])
> perf.mse[1] = abs(perf.se[1])
> for (i in 2:fx) {
+ perf.mape[i] = perf.mape[i-1] + abs(perf.pe[i])
+ perf.mape1[i] = perf.mape[i] / i
+ perf.mse[i] = perf.mse[i-1] + perf.se[i]
+ perf.mse1[i] = perf.mse[i] / i
+ }
> perf.rmse = sqrt(perf.mse1)
> postscript(file="/var/www/html/rcomp/tmp/2eybc1293200722.ps",horizontal=F,onefile=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:par1] <- 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/3wrb01293200723.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.mape1[i],4))
+ a<-table.element(a,round(perf.se[i],4))
+ a<-table.element(a,round(perf.mse1[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/4zsro1293200723.tab") 
> 
> try(system("convert tmp/18fei1293200722.ps tmp/18fei1293200722.png",intern=TRUE))
character(0)
> try(system("convert tmp/2eybc1293200722.ps tmp/2eybc1293200722.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  0.613   0.328   2.489