R version 2.9.0 (2009-04-17)
Copyright (C) 2009 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(10967.87,10433.56,10665.78,10666.71,10682.74,10777.22,10052.6,10213.97,10546.82,10767.2,10444.5,10314.68,9042.56,9220.75,9721.84,9978.53,9923.81,9892.56,10500.98,10179.35,10080.48,9492.44,8616.49,8685.4,8160.67,8048.1,8641.21,8526.63,8474.21,7916.13,7977.64,8334.59,8623.36,9098.03,9154.34,9284.73,9492.49,9682.35,9762.12,10124.63,10540.05,10601.61,10323.73,10418.4,10092.96,10364.91,10152.09,10032.8,10204.59,10001.6,10411.75,10673.38,10539.51,10723.78,10682.06,10283.19,10377.18,10486.64,10545.38,10554.27,10532.54,10324.31,10695.25,10827.81,10872.48,10971.19,11145.65,11234.68,11333.88,10997.97,11036.89,11257.35,11533.59,11963.12,12185.15,12377.62,12512.89,12631.48,12268.53,12754.8,13407.75,13480.21,13673.28,13239.71,13557.69,13901.28,13200.58,13406.97,12538.12,12419.57,12193.88,12656.63,12812.48,12056.67,11322.38,11530.75,11114.08,9181.73,8614.55,8595.56,8396.20,7690.50,7235.47,7992.12,8398.37,8593,8679.75,9374.63,9634.97,9857.34,10238.83)
> par10 = 'FALSE'
> par9 = '0'
> par8 = '0'
> par7 = '0'
> par6 = '1'
> par5 = '12'
> par4 = '0'
> 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:
         ar1
      0.1540
s.e.  0.1011

sigma^2 estimated as 164079:  log likelihood = -727.46,  aic = 1458.93
> (forecast <- predict(arima.out,par1))
$pred
Time Series:
Start = 100 
End = 111 
Frequency = 1 
 [1] 8527.232 8513.789 8511.720 8511.401 8511.352 8511.344 8511.343 8511.343
 [9] 8511.343 8511.343 8511.343 8511.343

$se
Time Series:
Start = 100 
End = 111 
Frequency = 1 
 [1]  405.0661  618.5194  781.1025  916.0176 1033.5731 1139.0750 1235.6033
 [8] 1325.1188 1408.9587 1488.0824 1563.2062 1634.8818

> (lb <- forecast$pred - 1.96 * forecast$se)
Time Series:
Start = 100 
End = 111 
Frequency = 1 
 [1] 7733.302 7301.491 6980.759 6716.006 6485.549 6278.757 6089.561 5914.110
 [9] 5749.784 5594.701 5447.459 5306.975
> (ub <- forecast$pred + 1.96 * forecast$se)
Time Series:
Start = 100 
End = 111 
Frequency = 1 
 [1]  9321.161  9726.087 10042.680 10306.795 10537.155 10743.931 10933.126
 [8] 11108.576 11272.902 11427.984 11575.227 11715.711
> 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$pred))
  [1] 10967.870 10433.560 10665.780 10666.710 10682.740 10777.220 10052.600
  [8] 10213.970 10546.820 10767.200 10444.500 10314.680  9042.560  9220.750
 [15]  9721.840  9978.530  9923.810  9892.560 10500.980 10179.350 10080.480
 [22]  9492.440  8616.490  8685.400  8160.670  8048.100  8641.210  8526.630
 [29]  8474.210  7916.130  7977.640  8334.590  8623.360  9098.030  9154.340
 [36]  9284.730  9492.490  9682.350  9762.120 10124.630 10540.050 10601.610
 [43] 10323.730 10418.400 10092.960 10364.910 10152.090 10032.800 10204.590
 [50] 10001.600 10411.750 10673.380 10539.510 10723.780 10682.060 10283.190
 [57] 10377.180 10486.640 10545.380 10554.270 10532.540 10324.310 10695.250
 [64] 10827.810 10872.480 10971.190 11145.650 11234.680 11333.880 10997.970
 [71] 11036.890 11257.350 11533.590 11963.120 12185.150 12377.620 12512.890
 [78] 12631.480 12268.530 12754.800 13407.750 13480.210 13673.280 13239.710
 [85] 13557.690 13901.280 13200.580 13406.970 12538.120 12419.570 12193.880
 [92] 12656.630 12812.480 12056.670 11322.380 11530.750 11114.080  9181.730
 [99]  8614.550  8527.232  8513.789  8511.720  8511.401  8511.352  8511.344
[106]  8511.343  8511.343  8511.343  8511.343  8511.343  8511.343
> (perc.se <- (ub-forecast$pred)/1.96/forecast$pred)
Time Series:
Start = 100 
End = 111 
Frequency = 1 
 [1] 0.04750265 0.07264913 0.09176788 0.10762243 0.12143465 0.13383021
 [7] 0.14517136 0.15568857 0.16553894 0.17483520 0.18366153 0.19208270
> postscript(file="/var/www/html/rcomp/tmp/1hwui1262194770.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> opar <- par(mar=c(4,4,2,2),las=1)
> ylim <- c( min(x[first:nx],lb), max(x[first:nx],ub))
> plot(x,ylim=ylim,type='n',xlim=c(first,lx))
> usr <- par('usr')
> rect(usr[1],usr[3],nx+1,usr[4],border=NA,col='lemonchiffon')
> rect(nx1,usr[3],usr[2],usr[4],border=NA,col='lavender')
> abline(h= (-3:3)*2 , col ='gray', lty =3)
> polygon( c(nx1:lx,lx:nx1), c(lb,rev(ub)), col = 'orange', lty=2,border=NA)
> lines(nx1:lx, lb , lty=2)
> lines(nx1:lx, ub , lty=2)
> lines(x, lwd=2)
> lines(nx1:lx, forecast$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.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/2796n1262194770.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: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/3327s1262194770.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/4iss01262194770.tab") 
> 
> try(system("convert tmp/1hwui1262194770.ps tmp/1hwui1262194770.png",intern=TRUE))
character(0)
> try(system("convert tmp/2796n1262194770.ps tmp/2796n1262194770.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  0.565   0.322   0.752