R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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> x <- c(1593,1477.9,1733.7,1569.7,1843.7,1950.3,1657.5,1772.1,1568.3,1809.8,1646.7,1808.5,1763.9,1625.5,1538.8,1342.4,1645.1,1619.9,1338.1,1505.5,1529.1,1511.9,1656.7,1694.4,1662.3,1588.7,1483.3,1585.6,1658.9,1584.4,1470.6,1618.7,1407.6,1473.9,1515.3,1485.4,1496.1,1493.5,1298.4,1375.3,1507.9,1455.3,1363.3,1392.8,1348.8,1880.3,1669.2,1543.6,1701.2,1516.5,1466.8,1484.1,1577.2,1684.5,1414.7,1674.5,1598.7,1739.1,1674.6,1671.8,1802,1526.8,1580.9,1634.8,1610.3,1712,1678.8,1708.1,1680.6,2056,1624,2021.4,1861.1,1750.8,1767.5,1710.3,2151.5,2047.9,1915.4,1984.7,1896.5,2170.8,2139.9,2330.5,2121.8,2226.8,1857.9,2155.9,2341.7,2290.2,2006.5,2111.9,1731.3,1762.2,1863.2,1943.5,1975.2)
> par10 = 'FALSE'
> par9 = '1'
> par8 = '0'
> par7 = '0'
> par6 = '2'
> 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: 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     sma1
      -0.7311  -0.4136  -0.9999
s.e.   0.1091   0.1130   0.3248

sigma^2 estimated as 11370:  log likelihood = -450.36,  aic = 908.73
> (forecast <- predict(arima.out,fx))
$pred
Time Series:
Start = 86 
End = 97 
Frequency = 1 
 [1] 2094.432 2085.937 2014.915 2230.648 2236.500 2051.742 2176.686 2088.125
 [9] 2312.950 2211.272 2311.954 2274.535

$se
Time Series:
Start = 86 
End = 97 
Frequency = 1 
 [1] 114.4023 118.3605 126.2383 143.9039 150.8110 159.6679 169.5259 176.8737
 [9] 184.8769 192.4911 199.3690 206.6848

> (lb <- forecast$pred - 1.96 * forecast$se)
Time Series:
Start = 86 
End = 97 
Frequency = 1 
 [1] 1870.204 1853.950 1767.488 1948.596 1940.911 1738.792 1844.415 1741.453
 [9] 1950.591 1833.990 1921.191 1869.433
> (ub <- forecast$pred + 1.96 * forecast$se)
Time Series:
Start = 86 
End = 97 
Frequency = 1 
 [1] 2318.661 2317.923 2262.342 2512.700 2532.090 2364.691 2508.956 2434.798
 [9] 2675.308 2588.555 2702.717 2679.637
> 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$pred))
 [1] 1593.000 1477.900 1733.700 1569.700 1843.700 1950.300 1657.500 1772.100
 [9] 1568.300 1809.800 1646.700 1808.500 1763.900 1625.500 1538.800 1342.400
[17] 1645.100 1619.900 1338.100 1505.500 1529.100 1511.900 1656.700 1694.400
[25] 1662.300 1588.700 1483.300 1585.600 1658.900 1584.400 1470.600 1618.700
[33] 1407.600 1473.900 1515.300 1485.400 1496.100 1493.500 1298.400 1375.300
[41] 1507.900 1455.300 1363.300 1392.800 1348.800 1880.300 1669.200 1543.600
[49] 1701.200 1516.500 1466.800 1484.100 1577.200 1684.500 1414.700 1674.500
[57] 1598.700 1739.100 1674.600 1671.800 1802.000 1526.800 1580.900 1634.800
[65] 1610.300 1712.000 1678.800 1708.100 1680.600 2056.000 1624.000 2021.400
[73] 1861.100 1750.800 1767.500 1710.300 2151.500 2047.900 1915.400 1984.700
[81] 1896.500 2170.800 2139.900 2330.500 2121.800 2094.432 2085.937 2014.915
[89] 2230.648 2236.500 2051.742 2176.686 2088.125 2312.950 2211.272 2311.954
[97] 2274.535
> (perc.se <- (ub-forecast$pred)/1.96/forecast$pred)
Time Series:
Start = 86 
End = 97 
Frequency = 1 
 [1] 0.05462210 0.05674215 0.06265192 0.06451217 0.06743170 0.07782068
 [7] 0.07788259 0.08470454 0.07993124 0.08704992 0.08623399 0.09086903
> postscript(file="/var/www/html/rcomp/tmp/1rjk41229364006.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> opar <- par(mar=c(4,4,2,2),las=1)
> ylim <- c( min(x[first:nx],lb), max(x[first:nx],ub))
> plot(x,ylim=ylim,type='n',xlim=c(first,lx))
> usr <- par('usr')
> rect(usr[1],usr[3],nx+1,usr[4],border=NA,col='lemonchiffon')
> rect(nx1,usr[3],usr[2],usr[4],border=NA,col='lavender')
> abline(h= (-3:3)*2 , col ='gray', lty =3)
> polygon( c(nx1:lx,lx:nx1), c(lb,rev(ub)), col = 'orange', lty=2,border=NA)
> lines(nx1:lx, lb , lty=2)
> lines(nx1:lx, ub , lty=2)
> lines(x, lwd=2)
> lines(nx1:lx, forecast$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/2mbrr1229364006.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/3z4z81229364007.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/4wn2t1229364007.tab") 
> 
> system("convert tmp/1rjk41229364006.ps tmp/1rjk41229364006.png")
> system("convert tmp/2mbrr1229364006.ps tmp/2mbrr1229364006.png")
> 
> 
> proc.time()
   user  system elapsed 
  0.818   0.345   3.480