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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Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- c(0.24,0.23,0.23,0.24,0.23,0.23,0.25,0.21,0.26,0.25,0.24,0.24,0.27,0.25,0.26,0.29,0.24,0.26,0.24,0.26,0.25,0.26,0.24,0.21,0.20,0.22,0.20,0.21,0.20,0.19,0.20,0.20,0.21,0.24,0.22,0.19,0.23,0.23,0.23,0.22,0.23,0.25,0.25,0.22,0.25,0.25,0.24,0.19,0.24,0.26,0.24,0.24,0.25,0.23,0.27,0.24,0.26,0.27,0.29,0.28,0.32,0.29,0.27,0.26,0.28,0.31,0.29,0.31,0.31,0.32,0.32,0.26,0.31,0.31,0.31,0.31,0.29,0.27,0.30,0.27,0.27,0.30,0.28,0.24,0.28,0.28,0.33,0.28,0.29,0.25,0.31,0.29,0.37,0.31,0.29,0.28,0.30,0.32,0.31,0.28,0.29,0.29,0.28,0.26,0.28,0.30,0.33,0.31,0.37,0.36,0.37,0.37,0.36,0.33,0.33,0.40,0.32,0.39,0.39,0.37,0.37,0.30,0.33,0.33,0.34,0.35,0.34,0.37,0.37,0.37,0.36,0.32,0.33,0.35,0.36,0.35,0.37,0.35,0.32,0.33,0.28,0.32,0.35,0.30,0.32,0.32,0.32,0.32,0.36,0.31,0.26,0.33,0.31,0.34,0.33,0.38,0.32,0.30,0.32,0.33,0.34,0.29,0.33,0.36,0.32,0.32,0.32,0.31,0.30,0.34,0.34,0.30,0.28,0.25,0.27,0.33,0.28,0.33,0.32,0.27,0.27,0.28,0.27,0.27,0.25,0.25,0.22,0.27)
> par20 = ''
> par19 = ''
> par18 = ''
> par17 = ''
> par16 = ''
> par15 = ''
> par14 = ''
> par13 = ''
> par12 = ''
> par11 = ''
> par10 = 'FALSE'
> par9 = '1'
> par8 = '0'
> par7 = '1'
> par6 = '0'
> par5 = '12'
> par4 = '1'
> par3 = '1'
> par2 = '-0.2'
> par1 = '12'
> ylab = ''
> xlab = ''
> main = ''
> #'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:
          ma1     sma1
      -0.5665  -0.7842
s.e.   0.0723   0.0627

sigma^2 estimated as 0.0005037:  log likelihood = 381.68,  aic = -757.35
> (forecast <- predict(arima.out,fx))
$pred
Time Series:
Start = 177 
End = 188 
Frequency = 1 
 [1] 1.280240 1.266525 1.266293 1.279380 1.274791 1.273355 1.267358 1.276317
 [9] 1.273187 1.295049 1.290771 1.266071

$se
Time Series:
Start = 177 
End = 188 
Frequency = 1 
 [1] 0.02244702 0.02446583 0.02633030 0.02807121 0.02970986 0.03126289
 [7] 0.03274234 0.03415777 0.03551684 0.03682578 0.03808977 0.03931315

> (lb <- forecast$pred - 1.96 * forecast$se)
Time Series:
Start = 177 
End = 188 
Frequency = 1 
 [1] 1.236244 1.218572 1.214685 1.224360 1.216560 1.212080 1.203183 1.209368
 [9] 1.203574 1.222871 1.216115 1.189017
> (ub <- forecast$pred + 1.96 * forecast$se)
Time Series:
Start = 177 
End = 188 
Frequency = 1 
 [1] 1.324236 1.314478 1.317900 1.334399 1.333022 1.334630 1.331533 1.343267
 [9] 1.342800 1.367228 1.365427 1.343125
> 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] 0.2400000 0.2300000 0.2300000 0.2400000 0.2300000 0.2300000 0.2500000
  [8] 0.2100000 0.2600000 0.2500000 0.2400000 0.2400000 0.2700000 0.2500000
 [15] 0.2600000 0.2900000 0.2400000 0.2600000 0.2400000 0.2600000 0.2500000
 [22] 0.2600000 0.2400000 0.2100000 0.2000000 0.2200000 0.2000000 0.2100000
 [29] 0.2000000 0.1900000 0.2000000 0.2000000 0.2100000 0.2400000 0.2200000
 [36] 0.1900000 0.2300000 0.2300000 0.2300000 0.2200000 0.2300000 0.2500000
 [43] 0.2500000 0.2200000 0.2500000 0.2500000 0.2400000 0.1900000 0.2400000
 [50] 0.2600000 0.2400000 0.2400000 0.2500000 0.2300000 0.2700000 0.2400000
 [57] 0.2600000 0.2700000 0.2900000 0.2800000 0.3200000 0.2900000 0.2700000
 [64] 0.2600000 0.2800000 0.3100000 0.2900000 0.3100000 0.3100000 0.3200000
 [71] 0.3200000 0.2600000 0.3100000 0.3100000 0.3100000 0.3100000 0.2900000
 [78] 0.2700000 0.3000000 0.2700000 0.2700000 0.3000000 0.2800000 0.2400000
 [85] 0.2800000 0.2800000 0.3300000 0.2800000 0.2900000 0.2500000 0.3100000
 [92] 0.2900000 0.3700000 0.3100000 0.2900000 0.2800000 0.3000000 0.3200000
 [99] 0.3100000 0.2800000 0.2900000 0.2900000 0.2800000 0.2600000 0.2800000
[106] 0.3000000 0.3300000 0.3100000 0.3700000 0.3600000 0.3700000 0.3700000
[113] 0.3600000 0.3300000 0.3300000 0.4000000 0.3200000 0.3900000 0.3900000
[120] 0.3700000 0.3700000 0.3000000 0.3300000 0.3300000 0.3400000 0.3500000
[127] 0.3400000 0.3700000 0.3700000 0.3700000 0.3600000 0.3200000 0.3300000
[134] 0.3500000 0.3600000 0.3500000 0.3700000 0.3500000 0.3200000 0.3300000
[141] 0.2800000 0.3200000 0.3500000 0.3000000 0.3200000 0.3200000 0.3200000
[148] 0.3200000 0.3600000 0.3100000 0.2600000 0.3300000 0.3100000 0.3400000
[155] 0.3300000 0.3800000 0.3200000 0.3000000 0.3200000 0.3300000 0.3400000
[162] 0.2900000 0.3300000 0.3600000 0.3200000 0.3200000 0.3200000 0.3100000
[169] 0.3000000 0.3400000 0.3400000 0.3000000 0.2800000 0.2500000 0.2700000
[176] 0.3300000 0.2907656 0.3068532 0.3071349 0.2917447 0.2970331 0.2987121
[183] 0.3058466 0.2952612 0.2989094 0.2745163 0.2790959 0.3074043
> (perc.se <- (ub-forecast$pred)/1.96/forecast$pred)
Time Series:
Start = 177 
End = 188 
Frequency = 1 
 [1] 0.09748324 0.10860518 0.11799245 0.12541266 0.13436843 0.14267910
 [7] 0.15137541 0.15774707 0.16563136 0.16942603 0.17704884 0.18817866
> postscript(file="/var/www/html/rcomp/tmp/1ydho1229425055.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> opar <- par(mar=c(4,4,2,2),las=1)
> ylim <- c( min(x[first:nx],lb), max(x[first:nx],ub))
> plot(x,ylim=ylim,type='n',xlim=c(first,lx))
> usr <- par('usr')
> rect(usr[1],usr[3],nx+1,usr[4],border=NA,col='lemonchiffon')
> rect(nx1,usr[3],usr[2],usr[4],border=NA,col='lavender')
> abline(h= (-3:3)*2 , col ='gray', lty =3)
> polygon( c(nx1:lx,lx:nx1), c(lb,rev(ub)), col = 'orange', lty=2,border=NA)
> lines(nx1:lx, lb , lty=2)
> lines(nx1:lx, ub , lty=2)
> lines(x, lwd=2)
> lines(nx1:lx, forecast$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/2v95e1229425055.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/3ubu01229425055.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/48xqr1229425055.tab") 
> 
> system("convert tmp/1ydho1229425055.ps tmp/1ydho1229425055.png")
> system("convert tmp/2v95e1229425055.ps tmp/2v95e1229425055.png")
> 
> 
> proc.time()
   user  system elapsed 
  0.823   0.318   0.934