R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)

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> x <- c(46,62,66,59,58,61,41,27,58,70,49,59,44,36,72,45,56,54,53,35,61,52,47,51,52,63,74,45,51,64,36,30,55,64,39,40,63,45,59,55,40,64,27,28,45,57,45,69,60,56,58,50,51,53,37,22,55,70,62,58,39,49,58,47,42,62,39,40,72,70,54,65)
> par10 = 'FALSE'
> par9 = '1'
> par8 = '2'
> par7 = '2'
> par6 = '3'
> par5 = '12'
> par4 = '1'
> par3 = '0'
> par2 = '2.0'
> 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      ar2      ar3      ma1     ma2    sar1    sar2     sma1
      0.5951  -0.0360  -0.1922  -0.6130  0.0968  0.0342  0.0340  -0.9997
s.e.  0.8450   0.6476   0.1561   0.8592  0.6407  0.2367  0.2777   0.6773

sigma^2 estimated as 696389:  log likelihood = -400.16,  aic = 818.31
> (forecast <- predict(arima.out,par1))
$pred
Time Series:
Start = 61 
End = 72 
Frequency = 1 
 [1] 2863.1794 2454.1284 4021.5325 2493.9665 2629.5728 3577.1104 1609.6695
 [8]  852.3171 3008.2652 3976.2697 2390.2574 3216.5247

$se
Time Series:
Start = 61 
End = 72 
Frequency = 1 
 [1] 911.6033 911.7384 912.8414 924.2316 928.0922 929.6427 929.6433 929.7712
 [9] 929.9062 929.6904 929.6682 929.6683

> (lb <- forecast$pred - 1.96 * forecast$se)
Time Series:
Start = 61 
End = 72 
Frequency = 1 
 [1] 1076.4369  667.1211 2232.3634  682.4726  810.5120 1755.0107 -212.4314
 [8] -970.0344 1185.6490 2154.0764  568.1077 1394.3749
> (ub <- forecast$pred + 1.96 * forecast$se)
Time Series:
Start = 61 
End = 72 
Frequency = 1 
 [1] 4649.922 4241.136 5810.702 4305.460 4448.634 5399.210 3431.770 2674.669
 [9] 4830.881 5798.463 4212.407 5038.674
> 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$pred))
 [1] 46.00000 62.00000 66.00000 59.00000 58.00000 61.00000 41.00000 27.00000
 [9] 58.00000 70.00000 49.00000 59.00000 44.00000 36.00000 72.00000 45.00000
[17] 56.00000 54.00000 53.00000 35.00000 61.00000 52.00000 47.00000 51.00000
[25] 52.00000 63.00000 74.00000 45.00000 51.00000 64.00000 36.00000 30.00000
[33] 55.00000 64.00000 39.00000 40.00000 63.00000 45.00000 59.00000 55.00000
[41] 40.00000 64.00000 27.00000 28.00000 45.00000 57.00000 45.00000 69.00000
[49] 60.00000 56.00000 58.00000 50.00000 51.00000 53.00000 37.00000 22.00000
[57] 55.00000 70.00000 62.00000 58.00000 53.50869 49.53916 63.41555 49.93963
[65] 51.27936 59.80895 40.12069 29.19447 54.84765 63.05767 48.89026 56.71441
> (perc.se <- (ub-forecast$pred)/1.96/forecast$pred)
Time Series:
Start = 61 
End = 72 
Frequency = 1 
 [1] 0.1399892 0.1605084 0.1030810 0.1601564 0.1534086 0.1166160 0.2347589
 [8] 0.3936084 0.1363414 0.1059119 0.1671048 0.1283661
> postscript(file="/var/www/rcomp/tmp/1ay2u1323208791.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))
Error in plot.window(...) : need finite 'ylim' values
Calls: plot -> plot.default -> localWindow -> plot.window
Execution halted