R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)

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> x <- c(12008,9169,8788,8417,8247,8197,8236,8253,7733,8366,8626,8863,10102,8463,9114,8563,8872,8301,8301,8278,7736,7973,8268,9476,11100,8962,9173,8738,8459,8078,8411,8291,7810,8616,8312,9692,9911,8915,9452,9112,8472,8230,8384,8625,8221,8649,8625,10443,10357,8586,8892,8329,8101,7922,8120,7838,7735,8406,8209,9451,10041,9411,10405,8467,8464,8102,7627,7513,7510,8291,8064,9383,9706,8579,9474,8318,8213,8059,9111,7708,7680,8014,8007,8718,9486,9113,9025,8476,7952,7759,7835,7600,7651,8319,8812,8630)
> par20 = ''
> par19 = ''
> par18 = ''
> par17 = ''
> par16 = ''
> par15 = ''
> par14 = ''
> par13 = ''
> par12 = ''
> par11 = ''
> par10 = 'FALSE'
> par9 = '1'
> par8 = '2'
> par7 = '1'
> par6 = '3'
> par5 = '12'
> par4 = '1'
> par3 = '0'
> par2 = '1'
> par1 = '24'
> 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     sar1     sar2    sma1
      -0.1153  0.3657  -0.1908  0.6640  -1.0160  -0.0838  0.7147
s.e.   0.3808  0.1931   0.1613  0.3486   0.5364   0.3541  0.5882

sigma^2 estimated as 152808:  log likelihood = -446.61,  aic = 909.22
> (forecast <- predict(arima.out,par1))
$pred
Time Series:
Start = 73 
End = 96 
Frequency = 1 
 [1] 10224.971  9087.296  9816.069  8295.530  8240.748  7963.303  7755.376
 [8]  7465.091  7478.564  8368.636  8035.285  9113.616  9833.486  9259.748
[15] 10293.657  8469.637  8454.687  8090.163  7670.356  7537.549  7531.054
[22]  8297.155  8078.144  9391.680

$se
Time Series:
Start = 73 
End = 96 
Frequency = 1 
 [1] 392.3613 447.2616 462.6510 462.7535 462.7662 463.5265 463.5677 463.6984
 [9] 463.7777 463.8196 463.8168 463.6931 539.4195 558.7894 565.1975 565.2697
[17] 565.2855 565.6387 565.6663 565.7341 565.7758 565.7984 565.8185 565.8306

> (lb <- forecast$pred - 1.96 * forecast$se)
Time Series:
Start = 73 
End = 96 
Frequency = 1 
 [1] 9455.943 8210.663 8909.273 7388.533 7333.727 7054.791 6846.783 6556.242
 [9] 6569.560 7459.549 7126.204 8204.778 8776.224 8164.521 9185.870 7361.709
[17] 7346.727 6981.511 6561.650 6428.710 6422.134 7188.190 6969.139 8282.652
> (ub <- forecast$pred + 1.96 * forecast$se)
Time Series:
Start = 73 
End = 96 
Frequency = 1 
 [1] 10993.999  9963.929 10722.865  9202.527  9147.770  8871.815  8663.969
 [8]  8373.940  8387.568  9277.722  8944.366 10022.455 10890.748 10354.976
[15] 11401.444  9577.566  9562.646  9198.815  8779.062  8646.388  8639.975
[22]  9406.120  9187.148 10500.708
> 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$pred))
 [1] 12008.000  9169.000  8788.000  8417.000  8247.000  8197.000  8236.000
 [8]  8253.000  7733.000  8366.000  8626.000  8863.000 10102.000  8463.000
[15]  9114.000  8563.000  8872.000  8301.000  8301.000  8278.000  7736.000
[22]  7973.000  8268.000  9476.000 11100.000  8962.000  9173.000  8738.000
[29]  8459.000  8078.000  8411.000  8291.000  7810.000  8616.000  8312.000
[36]  9692.000  9911.000  8915.000  9452.000  9112.000  8472.000  8230.000
[43]  8384.000  8625.000  8221.000  8649.000  8625.000 10443.000 10357.000
[50]  8586.000  8892.000  8329.000  8101.000  7922.000  8120.000  7838.000
[57]  7735.000  8406.000  8209.000  9451.000 10041.000  9411.000 10405.000
[64]  8467.000  8464.000  8102.000  7627.000  7513.000  7510.000  8291.000
[71]  8064.000  9383.000 10224.971  9087.296  9816.069  8295.530  8240.748
[78]  7963.303  7755.376  7465.091  7478.564  8368.636  8035.285  9113.616
[85]  9833.486  9259.748 10293.657  8469.637  8454.687  8090.163  7670.356
[92]  7537.549  7531.054  8297.155  8078.144  9391.680
> (perc.se <- (ub-forecast$pred)/1.96/forecast$pred)
Time Series:
Start = 73 
End = 96 
Frequency = 1 
 [1] 0.03837285 0.04921834 0.04713200 0.05578348 0.05615585 0.05820782
 [7] 0.05977372 0.06211558 0.06201427 0.05542357 0.05772251 0.05087915
[13] 0.05485537 0.06034607 0.05490736 0.06674072 0.06686060 0.06991685
[19] 0.07374707 0.07505545 0.07512571 0.06819186 0.07004313 0.06024808
> postscript(file="/var/wessaorg/rcomp/tmp/1tlno1354651706.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> opar <- par(mar=c(4,4,2,2),las=1)
> ylim <- c( min(x[first:nx],lb), max(x[first:nx],ub))
> plot(x,ylim=ylim,type='n',xlim=c(first,lx))
> usr <- par('usr')
> rect(usr[1],usr[3],nx+1,usr[4],border=NA,col='lemonchiffon')
> rect(nx1,usr[3],usr[2],usr[4],border=NA,col='lavender')
> abline(h= (-3:3)*2 , col ='gray', lty =3)
> polygon( c(nx1:lx,lx:nx1), c(lb,rev(ub)), col = 'orange', lty=2,border=NA)
> lines(nx1:lx, lb , lty=2)
> lines(nx1:lx, ub , lty=2)
> lines(x, lwd=2)
> lines(nx1:lx, forecast$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/wessaorg/rcomp/tmp/20wf81354651706.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/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/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/wessaorg/rcomp/tmp/35kiy1354651706.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/wessaorg/rcomp/tmp/4f4k61354651707.tab") 
> 
> try(system("convert tmp/1tlno1354651706.ps tmp/1tlno1354651706.png",intern=TRUE))
character(0)
> try(system("convert tmp/20wf81354651706.ps tmp/20wf81354651706.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  4.580   1.476   6.058