R version 3.4.3 (2017-11-30) -- "Kite-Eating Tree"
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Platform: x86_64-pc-linux-gnu (64-bit)

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> source('/home/pw/wessanet/cretab')
> 
> 
> 
> myrfcuid = 'r0386985'
> 
> x <- c(62.4,67.4,76.1,67.4,74.5,72.6,60.5,66.1,76.5,76.8,77,71,74.8,73.7,80.5,71.8,76.9,79.9,65.9,69.5,75.1,79.6,75.2,68,72.8,71.5,78.5,76.8,75.3,76.7,69.7,67.8,77.5,82.5,75.3,70.9,76,73.7,79.7,77.8,73.3,78.3,71.9,67,82,83.7,74.8,80,74.3,76.8,89,81.9,76.8,88.9,75.8,75.5,89.1,88,85.9,89.3,82.9,81.2,90.5,86.4,81.8,91.3,73.4,76.6,91,87,89.7,90.7,86.5,86.6,98.8,84.4,91.4,95.7,78.5,81.7,94.3,98.5,95.4,91.7,92.8,90.5,102.2,91.8,95,102,88.9,89.6,97.9,108.6,100.8,95.1,101,100.9,102.5,105.4,98.4,105.3,96.5,88.1,107.9,107,92.5,95.7,85.2,85.5,94.7,86.2,88.8,93.4,83.4,82.9,96.7,96.2,92.8,92.8,90,95.4,108.3,96.3,95,109,92,92.3,107,105.5,105.4,103.9,99.2,102.2,121.5,102.3,110,105.9,91.9,100,111.7,104.9,103.3,101.8,100.8,104.2,116.5,97.9,100.7,107,96.3,96,104.5,107.4,102.4,94.9,98.8,96.8,108.2,103.8,102.3,107.2,102,92.6,105.2,113,105.6,101.6,101.7,102.7,109,105.5,103.3,108.6,98.2,90,112.4,111.9,102.1,102.4,101.7,98.7,114,105.1,98.3,110,96.5,92.2,112,111.4,107.5,103.4,103.5,107.4,117.6,110.2,104.3,115.9,98.9,101.9,113.5,109.5,110,114.2,106.9,109.2,124.2,104.7,111.9,119,102.9,106.3)
> par10 = 'FALSE'
> par9 = '1'
> par8 = '2'
> par7 = '1'
> par6 = '0'
> par5 = '12'
> par4 = '1'
> par3 = '0'
> par2 = '1'
> par1 = '0'
> par10 <- 'FALSE'
> par9 <- '1'
> par8 <- '2'
> par7 <- '1'
> par6 <- '0'
> par5 <- '12'
> par4 <- '1'
> par3 <- '0'
> par2 <- '1'
> par1 <- '0'
> #'GNU S' R Code compiled by R2WASP v. 1.2.327 (Wed, 16 Nov 2016 12:47:20 +0100)
> #Author: root
> #To cite this work: Wessa P., (2016), ARIMA Forecasting (v1.0.10) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_arimaforecasting.wasp/
> #Source of accompanying publication: 
> #
> 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*2
+ 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:
         ma1     sar1     sar2     sma1
      0.5029  -0.1031  -0.1359  -0.0693
s.e.  0.0702   0.5915   0.1142   0.6073

sigma^2 estimated as 29.03:  log likelihood = -621.16,  aic = 1252.31
> (forecast <- predict(arima.out,fx))
$pred
Time Series:
Start = 213 
End = 236 
Frequency = 1 
 [1] 115.1796 109.9088 108.8041 112.1768 106.0455 107.6404 122.4780 104.8972
 [9] 109.7526 117.5776 101.8751 104.0864 114.6671 110.1248 108.5877 110.9178
[17] 105.6716 107.5566 121.7587 105.6242 108.9413 117.3031 101.4372 103.7168

$se
Time Series:
Start = 213 
End = 236 
Frequency = 1 
 [1] 5.387782 6.030759 6.030759 6.030759 6.030759 6.030759 6.030759 6.030759
 [9] 6.030759 6.030759 6.030759 6.030759 7.499908 7.827921 7.827921 7.827921
[17] 7.827921 7.827921 7.827921 7.827921 7.827921 7.827921 7.827921 7.827921

> (lb <- forecast$pred - 1.96 * forecast$se)
Time Series:
Start = 213 
End = 236 
Frequency = 1 
 [1] 104.61960  98.08851  96.98378 100.35654  94.22520  95.82011 110.65772
 [8]  93.07688  97.93230 105.75736  90.05481  92.26614  99.96733  94.78211
[15]  93.24496  95.57511  90.32886  92.21392 106.41599  90.28152  93.59855
[22] 101.96035  86.09450  88.37407
> (ub <- forecast$pred + 1.96 * forecast$se)
Time Series:
Start = 213 
End = 236 
Frequency = 1 
 [1] 125.7397 121.7291 120.6244 123.9971 117.8658 119.4607 134.2983 116.7175
 [9] 121.5729 129.3979 113.6954 115.9067 129.3670 125.4676 123.9304 126.2606
[17] 121.0143 122.8994 137.1014 120.9670 124.2840 132.6458 116.7800 119.0595
> 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]  62.4000  67.4000  76.1000  67.4000  74.5000  72.6000  60.5000  66.1000
  [9]  76.5000  76.8000  77.0000  71.0000  74.8000  73.7000  80.5000  71.8000
 [17]  76.9000  79.9000  65.9000  69.5000  75.1000  79.6000  75.2000  68.0000
 [25]  72.8000  71.5000  78.5000  76.8000  75.3000  76.7000  69.7000  67.8000
 [33]  77.5000  82.5000  75.3000  70.9000  76.0000  73.7000  79.7000  77.8000
 [41]  73.3000  78.3000  71.9000  67.0000  82.0000  83.7000  74.8000  80.0000
 [49]  74.3000  76.8000  89.0000  81.9000  76.8000  88.9000  75.8000  75.5000
 [57]  89.1000  88.0000  85.9000  89.3000  82.9000  81.2000  90.5000  86.4000
 [65]  81.8000  91.3000  73.4000  76.6000  91.0000  87.0000  89.7000  90.7000
 [73]  86.5000  86.6000  98.8000  84.4000  91.4000  95.7000  78.5000  81.7000
 [81]  94.3000  98.5000  95.4000  91.7000  92.8000  90.5000 102.2000  91.8000
 [89]  95.0000 102.0000  88.9000  89.6000  97.9000 108.6000 100.8000  95.1000
 [97] 101.0000 100.9000 102.5000 105.4000  98.4000 105.3000  96.5000  88.1000
[105] 107.9000 107.0000  92.5000  95.7000  85.2000  85.5000  94.7000  86.2000
[113]  88.8000  93.4000  83.4000  82.9000  96.7000  96.2000  92.8000  92.8000
[121]  90.0000  95.4000 108.3000  96.3000  95.0000 109.0000  92.0000  92.3000
[129] 107.0000 105.5000 105.4000 103.9000  99.2000 102.2000 121.5000 102.3000
[137] 110.0000 105.9000  91.9000 100.0000 111.7000 104.9000 103.3000 101.8000
[145] 100.8000 104.2000 116.5000  97.9000 100.7000 107.0000  96.3000  96.0000
[153] 104.5000 107.4000 102.4000  94.9000  98.8000  96.8000 108.2000 103.8000
[161] 102.3000 107.2000 102.0000  92.6000 105.2000 113.0000 105.6000 101.6000
[169] 101.7000 102.7000 109.0000 105.5000 103.3000 108.6000  98.2000  90.0000
[177] 112.4000 111.9000 102.1000 102.4000 101.7000  98.7000 114.0000 105.1000
[185]  98.3000 110.0000  96.5000  92.2000 112.0000 111.4000 107.5000 103.4000
[193] 103.5000 107.4000 117.6000 110.2000 104.3000 115.9000  98.9000 101.9000
[201] 113.5000 109.5000 110.0000 114.2000 106.9000 109.2000 124.2000 104.7000
[209] 111.9000 119.0000 102.9000 106.3000 115.1796 109.9088 108.8041 112.1768
[217] 106.0455 107.6404 122.4780 104.8972 109.7526 117.5776 101.8751 104.0864
[225] 114.6671 110.1248 108.5877 110.9178 105.6716 107.5566 121.7587 105.6242
[233] 108.9413 117.3031 101.4372 103.7168
> (perc.se <- (ub-forecast$pred)/1.96/forecast$pred)
Time Series:
Start = 213 
End = 236 
Frequency = 1 
 [1] 0.04677720 0.05487058 0.05542770 0.05376118 0.05686955 0.05602691
 [7] 0.04923952 0.05749211 0.05494867 0.05129172 0.05919758 0.05793992
[13] 0.06540590 0.07108225 0.07208848 0.07057405 0.07407783 0.07277952
[19] 0.06429044 0.07411102 0.07185450 0.06673245 0.07717010 0.07547399
> postscript(file="/home/pw/wessanet/rcomp/tmp/1rg6v1517477546.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> opar <- par(mar=c(4,4,2,2),las=1)
> ylim <- c( min(x[first:nx],lb), max(x[first:nx],ub))
> plot(x,ylim=ylim,type='n',xlim=c(first,lx))
> usr <- par('usr')
> rect(usr[1],usr[3],nx+1,usr[4],border=NA,col='lemonchiffon')
> rect(nx1,usr[3],usr[2],usr[4],border=NA,col='lavender')
> abline(h= (-3:3)*2 , col ='gray', lty =3)
> polygon( c(nx1:lx,lx:nx1), c(lb,rev(ub)), col = 'orange', lty=2,border=NA)
> lines(nx1:lx, lb , lty=2)
> lines(nx1:lx, ub , lty=2)
> lines(x, lwd=2)
> lines(nx1:lx, forecast$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.spe <- array(0, dim=fx)
> perf.scalederr <- array(0, dim=fx)
> perf.mase <- array(0, dim=fx)
> perf.mase1 <- array(0, dim=fx)
> perf.mape <- array(0, dim=fx)
> perf.smape <- array(0, dim=fx)
> perf.mape1 <- array(0, dim=fx)
> perf.smape1 <- 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)
> perf.scaleddenom <- 0
> for (i in 2:fx) {
+ perf.scaleddenom = perf.scaleddenom + abs(x[nx+i] - x[nx+i-1])
+ }
> perf.scaleddenom = perf.scaleddenom / (fx-1)
> for (i in 1:fx) {
+ locSD <- (ub[i] - forecast$pred[i]) / 1.96
+ perf.scalederr[i] = (x[nx+i] - forecast$pred[i]) / perf.scaleddenom
+ perf.pe[i] = (x[nx+i] - forecast$pred[i]) / x[nx+i]
+ perf.spe[i] = 2*(x[nx+i] - forecast$pred[i]) / (x[nx+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.smape[1] = abs(perf.spe[1])
> perf.mape1[1] = perf.mape[1]
> perf.smape1[1] = perf.smape[1]
> perf.mse[1] = perf.se[1]
> perf.mase[1] = abs(perf.scalederr[1])
> perf.mase1[1] = perf.mase[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.smape[i] = perf.smape[i-1] + abs(perf.spe[i])
+ perf.smape1[i] = perf.smape[i] / i
+ perf.mse[i] = perf.mse[i-1] + perf.se[i]
+ perf.mse1[i] = perf.mse[i] / i
+ perf.mase[i] = perf.mase[i-1] + abs(perf.scalederr[i])
+ perf.mase1[i] = perf.mase[i] / i
+ }
> perf.rmse = sqrt(perf.mse1)
> postscript(file="/home/pw/wessanet/rcomp/tmp/22qph1517477546.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]
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 
> 
> 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="/home/pw/wessanet/rcomp/tmp/3ssi41517477546.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Univariate ARIMA Extrapolation Forecast Performance',10,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,'sMAPE',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.element(a,'ScaledE',1,header=TRUE)
> a<-table.element(a,'MASE',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.smape1[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.element(a,round(perf.scalederr[i],4))
+ a<-table.element(a,round(perf.mase1[i],4))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/home/pw/wessanet/rcomp/tmp/4cmye1517477546.tab") 
> 
> try(system("convert /home/pw/wessanet/rcomp/tmp/1rg6v1517477546.ps /home/pw/wessanet/rcomp/tmp/1rg6v1517477546.png",intern=TRUE))
character(0)
> try(system("convert /home/pw/wessanet/rcomp/tmp/22qph1517477546.ps /home/pw/wessanet/rcomp/tmp/22qph1517477546.png",intern=TRUE))
character(0)
> 
> proc.time()
   user  system elapsed 
  3.344   0.328   3.761