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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You are welcome to redistribute it under certain conditions.
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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(236.422
+ ,250.580
+ ,279.515
+ ,264.417
+ ,283.706
+ ,281.288
+ ,271.146
+ ,283.944
+ ,269.155
+ ,270.899
+ ,276.507
+ ,319.957
+ ,250.746
+ ,247.772
+ ,280.449
+ ,274.925
+ ,296.013
+ ,287.881
+ ,279.098
+ ,294.763
+ ,261.924
+ ,291.596
+ ,287.537
+ ,326.201
+ ,255.598
+ ,253.086
+ ,285.261
+ ,284.747
+ ,300.402
+ ,288.854
+ ,295.433
+ ,307.256
+ ,273.189
+ ,287.540
+ ,290.705
+ ,337.006
+ ,268.335
+ ,259.060
+ ,293.703
+ ,294.262
+ ,312.404
+ ,301.014
+ ,309.942
+ ,317.079
+ ,293.912
+ ,304.060
+ ,301.299
+ ,357.634
+ ,281.493
+ ,282.478
+ ,319.111
+ ,315.223
+ ,328.445
+ ,321.081
+ ,328.040
+ ,326.362
+ ,313.566
+ ,319.768
+ ,324.315
+ ,387.243
+ ,293.308
+ ,295.109
+ ,339.190
+ ,335.678
+ ,345.401
+ ,351.002
+ ,351.889
+ ,355.773
+ ,333.363
+ ,336.214
+ ,343.910
+ ,405.788
+ ,318.682
+ ,314.189
+ ,362.141
+ ,351.811
+ ,373.727
+ ,366.795
+ ,362.393
+ ,376.006
+ ,346.423
+ ,349.007
+ ,357.224
+ ,418.473
+ ,329.169
+ ,323.456
+ ,374.439
+ ,358.806
+ ,391.816
+ ,376.944
+ ,372.665
+ ,388.357
+ ,354.241
+ ,368.982
+ ,378.233
+ ,426.699
+ ,343.241
+ ,344.577
+ ,373.623
+ ,369.688
+ ,398.816
+ ,379.387
+ ,384.666
+ ,383.879
+ ,351.578
+ ,350.920
+ ,336.629
+ ,385.504
+ ,311.330
+ ,300.545
+ ,329.718
+ ,331.023
+ ,348.944
+ ,345.650
+ ,349.260
+ ,354.597
+ ,325.769
+ ,339.734
+ ,340.543
+ ,401.585
+ ,315.998
+ ,312.327
+ ,362.217
+ ,358.067
+ ,367.321
+ ,360.372
+ ,363.830
+ ,364.525
+ ,347.945
+ ,357.404
+ ,368.182
+ ,429.343)
> par10 = 'FALSE'
> par9 = '1'
> par8 = '0'
> par7 = '1'
> par6 = '2'
> par5 = '12'
> par4 = '1'
> par3 = '1'
> par2 = '0.5'
> par1 = '12'
> 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     ma1     sma1
      -0.8670  -0.4699  0.3799  -0.8165
s.e.   0.1834   0.0977  0.1978   0.1447

sigma^2 estimated as 0.03443:  log likelihood = 21.68,  aic = -33.35
> (forecast <- predict(arima.out,par1))
$pred
Time Series:
Start = 121 
End = 132 
Frequency = 1 
 [1] 17.78830 17.67256 18.76486 18.58320 19.15573 18.94402 18.95631 19.16634
 [9] 18.41613 18.65643 18.69648 20.12385

$se
Time Series:
Start = 121 
End = 132 
Frequency = 1 
 [1] 0.1863167 0.2093576 0.2265543 0.2646851 0.2820565 0.3020228 0.3240344
 [8] 0.3404024 0.3581076 0.3750377 0.3902590 0.4056923

> (lb <- forecast$pred - 1.96 * forecast$se)
Time Series:
Start = 121 
End = 132 
Frequency = 1 
 [1] 17.42312 17.26222 18.32081 18.06442 18.60290 18.35206 18.32120 18.49915
 [9] 17.71424 17.92135 17.93158 19.32869
> (ub <- forecast$pred + 1.96 * forecast$se)
Time Series:
Start = 121 
End = 132 
Frequency = 1 
 [1] 18.15348 18.08290 19.20891 19.10198 19.70856 19.53599 19.59142 19.83353
 [9] 19.11802 19.39150 19.46139 20.91900
> 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$pred))
  [1] 236.4220 250.5800 279.5150 264.4170 283.7060 281.2880 271.1460 283.9440
  [9] 269.1550 270.8990 276.5070 319.9570 250.7460 247.7720 280.4490 274.9250
 [17] 296.0130 287.8810 279.0980 294.7630 261.9240 291.5960 287.5370 326.2010
 [25] 255.5980 253.0860 285.2610 284.7470 300.4020 288.8540 295.4330 307.2560
 [33] 273.1890 287.5400 290.7050 337.0060 268.3350 259.0600 293.7030 294.2620
 [41] 312.4040 301.0140 309.9420 317.0790 293.9120 304.0600 301.2990 357.6340
 [49] 281.4930 282.4780 319.1110 315.2230 328.4450 321.0810 328.0400 326.3620
 [57] 313.5660 319.7680 324.3150 387.2430 293.3080 295.1090 339.1900 335.6780
 [65] 345.4010 351.0020 351.8890 355.7730 333.3630 336.2140 343.9100 405.7880
 [73] 318.6820 314.1890 362.1410 351.8110 373.7270 366.7950 362.3930 376.0060
 [81] 346.4230 349.0070 357.2240 418.4730 329.1690 323.4560 374.4390 358.8060
 [89] 391.8160 376.9440 372.6650 388.3570 354.2410 368.9820 378.2330 426.6990
 [97] 343.2410 344.5770 373.6230 369.6880 398.8160 379.3870 384.6660 383.8790
[105] 351.5780 350.9200 336.6290 385.5040 311.3300 300.5450 329.7180 331.0230
[113] 348.9440 345.6500 349.2600 354.5970 325.7690 339.7340 340.5430 401.5850
[121] 316.4237 312.3193 352.1199 345.3353 366.9418 358.8760 359.3416 367.3486
[129] 339.1538 348.0623 349.5585 404.9692
> (perc.se <- (ub-forecast$pred)/1.96/forecast$pred)
Time Series:
Start = 121 
End = 132 
Frequency = 1 
 [1] 0.02116325 0.02396802 0.02443236 0.02888412 0.02987374 0.03238400
 [7] 0.03476020 0.03613910 0.03963177 0.04099671 0.04260075 0.04111613
> postscript(file="/var/wessaorg/rcomp/tmp/1mniz1353764625.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> opar <- par(mar=c(4,4,2,2),las=1)
> ylim <- c( min(x[first:nx],lb), max(x[first:nx],ub))
> plot(x,ylim=ylim,type='n',xlim=c(first,lx))
> usr <- par('usr')
> rect(usr[1],usr[3],nx+1,usr[4],border=NA,col='lemonchiffon')
> rect(nx1,usr[3],usr[2],usr[4],border=NA,col='lavender')
> abline(h= (-3:3)*2 , col ='gray', lty =3)
> polygon( c(nx1:lx,lx:nx1), c(lb,rev(ub)), col = 'orange', lty=2,border=NA)
> lines(nx1:lx, lb , lty=2)
> lines(nx1:lx, ub , lty=2)
> lines(x, lwd=2)
> lines(nx1:lx, forecast$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/2ov7t1353764626.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/32qxb1353764626.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/4eue11353764626.tab") 
> 
> try(system("convert tmp/1mniz1353764625.ps tmp/1mniz1353764625.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ov7t1353764626.ps tmp/2ov7t1353764626.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.839   0.274   2.159