R version 2.6.0 (2007-10-03)
Copyright (C) 2007 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> x <- c(95.4,101.2,101.5,101.9,101.7,100.1,97.4,96.5,99.2,102.2,105.3,111.1,114.9,124.5,142.2,159.7,165.2,198.6,207.8,219.6,239.6,235.3,218.5,213.8,205.5,198.4,198.5,190.2,180.7,193.6,192.8,195.5,197.2,196.9,178.9,172.4,156.4,143.7,153.6,168.8,185.8,199.9,205.4,197.5,199.6,200.5,193.7,179.6,169.1,169.8,195.5,194.8,204.5,203.8,204.8,204.9,240.0,248.3,258.4,254.9)
> par10 = 'FALSE'
> par9 = '1'
> par8 = '2'
> par7 = '2'
> par6 = '3'
> par5 = '12'
> par4 = '0'
> par3 = '1'
> par2 = '-0.6'
> 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
      0.2290  -0.3183  0.6470  0.2602  1.0000  0.0102  -0.9999  0.0972
s.e.  0.1726   0.2177  0.1603  0.0035  0.0033  0.0135   0.0002  1.0539

sigma^2 estimated as 2.618e-10:  log likelihood = 126.05,  aic = -234.1
> (forecast <- predict(arima.out,fx))
$pred
Time Series:
Start = 25 
End = 60 
Frequency = 1 
 [1] 0.04292872 0.04515955 0.04522747 0.04533980 0.04525693 0.04461006
 [7] 0.04355560 0.04318430 0.04422232 0.04534961 0.04647381 0.04841567
[13] 0.04962887 0.05238017 0.05662153 0.06005848 0.06101573 0.06589201
[19] 0.06700166 0.06832381 0.07033964 0.06994317 0.06823874 0.06774024
[25] 0.06481830 0.06261581 0.06259124 0.06251407 0.06260672 0.06330335
[31] 0.06436905 0.06475383 0.06373651 0.06260528 0.06146378 0.05951703

$se
Time Series:
Start = 25 
End = 60 
Frequency = 1 
 [1] 2.206703e-05 3.591446e-05 5.722244e-05 8.023858e-05 9.844014e-05
 [6] 1.163360e-04 1.362682e-04 1.537538e-04 1.695737e-04 1.865755e-04
[11] 2.026570e-04 2.170706e-04 2.320951e-04 2.469967e-04 2.609427e-04
[16] 2.747887e-04 2.885779e-04 3.016201e-04 3.142682e-04 3.268524e-04
[21] 3.389495e-04 3.505844e-04 3.620932e-04 3.733050e-04 3.786857e-04
[26] 3.817666e-04 3.836221e-04 3.839835e-04 3.848978e-04 3.857821e-04
[31] 3.859301e-04 3.861774e-04 3.866128e-04 3.867287e-04 3.867998e-04
[36] 3.870193e-04

> (lb <- forecast$pred - 1.96 * forecast$se)
Time Series:
Start = 25 
End = 60 
Frequency = 1 
 [1] 0.04288547 0.04508916 0.04511531 0.04518253 0.04506398 0.04438204
 [7] 0.04328851 0.04288294 0.04388995 0.04498393 0.04607661 0.04799022
[13] 0.04917396 0.05189606 0.05611008 0.05951990 0.06045012 0.06530083
[19] 0.06638569 0.06768318 0.06967530 0.06925603 0.06752904 0.06700856
[25] 0.06407608 0.06186755 0.06183934 0.06176146 0.06185232 0.06254721
[31] 0.06361263 0.06399693 0.06297875 0.06184730 0.06070566 0.05875847
> (ub <- forecast$pred + 1.96 * forecast$se)
Time Series:
Start = 25 
End = 60 
Frequency = 1 
 [1] 0.04297197 0.04522994 0.04533963 0.04549707 0.04544987 0.04483808
 [7] 0.04382268 0.04348565 0.04455468 0.04571530 0.04687102 0.04884113
[13] 0.05008378 0.05286428 0.05713298 0.06059707 0.06158135 0.06648318
[19] 0.06761762 0.06896444 0.07100399 0.07063032 0.06894845 0.06847192
[25] 0.06556052 0.06336407 0.06334314 0.06326668 0.06336112 0.06405948
[31] 0.06512547 0.06551074 0.06449428 0.06336327 0.06222191 0.06027558
> 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$pred))
 [1]  95.40000 101.20000 101.50000 101.90000 101.70000 100.10000  97.40000
 [8]  96.50000  99.20000 102.20000 105.30000 111.10000 114.90000 124.50000
[15] 142.20000 159.70000 165.20000 198.60000 207.80000 219.60000 239.60000
[22] 235.30000 218.50000 213.80000 189.99994 174.61598 174.17915 173.46053
[29] 173.99025 178.21544 185.46418 188.12951 180.82739 173.39798 166.46367
[36] 155.48555 149.20247 136.37094 119.77439 108.56989 105.74592  93.02766
[43]  90.47406  87.57494  83.43208  84.22178  87.75697  88.83595  95.61014
[50] 101.28070 101.34696 101.55557 101.30521  99.45399  96.72487  95.76883
[57]  98.33002 101.30907 104.46429 110.22107
> (perc.se <- (ub-forecast$pred)/1.96/forecast$pred)
Time Series:
Start = 25 
End = 60 
Frequency = 1 
 [1] -0.0008555819 -0.0013227160 -0.0021017394 -0.0029359436 -0.0036047325
 [6] -0.0043169647 -0.0051720252 -0.0058792689 -0.0063274959 -0.0067839273
[11] -0.0071858166 -0.0073858402 -0.0077001551 -0.0077633373 -0.0075894082
[16] -0.0075353981 -0.0077862727 -0.0075388777 -0.0077226643 -0.0078745706
[21] -0.0079312759 -0.0082458975 -0.0087226871 -0.0090541662 -0.0095904988
[26] -0.0100020360 -0.0100537640 -0.0100753115 -0.0100842136 -0.0099975389
[31] -0.0098382908 -0.0097868982 -0.0099517021 -0.0101316469 -0.0103186246
[36] -0.0106564290
> postscript(file="/var/www/html/rcomp/tmp/1qlv11197122571.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> opar <- par(mar=c(4,4,2,2),las=1)
> ylim <- c( min(x[first:nx],lb), max(x[first:nx],ub))
> plot(x,ylim=ylim,type='n',xlim=c(first,lx))
> usr <- par('usr')
> rect(usr[1],usr[3],nx+1,usr[4],border=NA,col='lemonchiffon')
> rect(nx1,usr[3],usr[2],usr[4],border=NA,col='lavender')
> abline(h= (-3:3)*2 , col ='gray', lty =3)
> polygon( c(nx1:lx,lx:nx1), c(lb,rev(ub)), col = 'orange', lty=2,border=NA)
> lines(nx1:lx, lb , lty=2)
> lines(nx1:lx, ub , lty=2)
> lines(x, lwd=2)
> lines(nx1:lx, forecast$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/2k2nd1197122572.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/3ksyp1197122572.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/4mv7i1197122572.tab") 
> 
> system("convert tmp/1qlv11197122571.ps tmp/1qlv11197122571.png")
> system("convert tmp/2k2nd1197122572.ps tmp/2k2nd1197122572.png")
> 
> 
> proc.time()
   user  system elapsed 
  3.230   0.874   3.895