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(277
+ ,260.6
+ ,291.6
+ ,275.4
+ ,275.3
+ ,231.7
+ ,238.8
+ ,274.2
+ ,277.8
+ ,299.1
+ ,286.6
+ ,232.3
+ ,294.1
+ ,267.5
+ ,309.7
+ ,280.7
+ ,287.3
+ ,235.7
+ ,256.4
+ ,289
+ ,290.8
+ ,321.9
+ ,291.8
+ ,241.4
+ ,295.5
+ ,258.2
+ ,306.1
+ ,281.5
+ ,283.1
+ ,237.4
+ ,274.8
+ ,299.3
+ ,300.4
+ ,340.9
+ ,318.8
+ ,265.7
+ ,322.7
+ ,281.6
+ ,323.5
+ ,312.6
+ ,310.8
+ ,262.8
+ ,273.8
+ ,320
+ ,310.3
+ ,342.2
+ ,320.1
+ ,265.6
+ ,327
+ ,300.7
+ ,346.4
+ ,317.3
+ ,326.2
+ ,270.7
+ ,278.2
+ ,324.6
+ ,321.8
+ ,343.5
+ ,354
+ ,278.2
+ ,330.2
+ ,307.3
+ ,375.9
+ ,335.3
+ ,339.3
+ ,280.3
+ ,293.7
+ ,341.2
+ ,345.1
+ ,368.7
+ ,369.4
+ ,288.4
+ ,341
+ ,319.1
+ ,374.2
+ ,344.5
+ ,337.3
+ ,281
+ ,282.2
+ ,321
+ ,325.4
+ ,366.3
+ ,380.3
+ ,300.7
+ ,359.3
+ ,327.6
+ ,383.6
+ ,352.4
+ ,329.4
+ ,294.5
+ ,333.5
+ ,334.3
+ ,358
+ ,396.1
+ ,387
+ ,307.2
+ ,363.9
+ ,344.7
+ ,397.6
+ ,376.8
+ ,337.1
+ ,299.3
+ ,323.1
+ ,329.1
+ ,347
+ ,462
+ ,436.5
+ ,360.4
+ ,415.5
+ ,382.1
+ ,432.2
+ ,424.3
+ ,386.7
+ ,354.5
+ ,375.8
+ ,368
+ ,402.4
+ ,426.5
+ ,433.3
+ ,338.5
+ ,416.8
+ ,381.1
+ ,445.7
+ ,412.4
+ ,394
+ ,348.2
+ ,380.1
+ ,373.7
+ ,393.6
+ ,434.2
+ ,430.7
+ ,344.5
+ ,411.9
+ ,370.5
+ ,437.3
+ ,411.3
+ ,385.5
+ ,341.3
+ ,384.2
+ ,373.2
+ ,415.8
+ ,448.6
+ ,454.3
+ ,350.3
+ ,419.1
+ ,398
+ ,456.1
+ ,430.1
+ ,399.8
+ ,362.7
+ ,384.9
+ ,385.3
+ ,432.3
+ ,468.9
+ ,442.7
+ ,370.2
+ ,439.4
+ ,393.9
+ ,468.7
+ ,438.8
+ ,430.1
+ ,366.3
+ ,391
+ ,380.9
+ ,431.4
+ ,465.4
+ ,471.5
+ ,387.5
+ ,446.4
+ ,421.5
+ ,504.8
+ ,492.1
+ ,421.3
+ ,396.7
+ ,428
+ ,421.9
+ ,465.6
+ ,525.8
+ ,499.9
+ ,435.3
+ ,479.5
+ ,473
+ ,554.4
+ ,489.6
+ ,462.2
+ ,420.3)
> par10 = 'FALSE'
> par9 = '1'
> par8 = '0'
> par7 = '0'
> par6 = '3'
> par5 = '12'
> par4 = '1'
> par3 = '1'
> par2 = '0.0'
> 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
> par7 <- 3
> 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      ma3     sma1
      0.7654  -0.4976  0.2728  -1.3960  0.9904  -0.5886  -0.5606
s.e.  0.4058   0.3392  0.1466   0.3924  0.5481   0.2050   0.0768

sigma^2 estimated as 0.001141:  log likelihood = 312.54,  aic = -609.08
> (forecast <- predict(arima.out,par1))
$pred
Time Series:
Start = 175 
End = 186 
Frequency = 1 
 [1] 6.035391 6.005346 6.118845 6.208861 6.198468 5.989211 6.150372 6.075961
 [9] 6.244148 6.197211 6.102539 6.002135

$se
Time Series:
Start = 175 
End = 186 
Frequency = 1 
 [1] 0.03383858 0.03611470 0.03838437 0.04060257 0.04127630 0.04138118
 [7] 0.04145519 0.04154705 0.04159348 0.04160644 0.04161424 0.04162367

> (lb <- forecast$pred - 1.96 * forecast$se)
Time Series:
Start = 175 
End = 186 
Frequency = 1 
 [1] 5.969068 5.934561 6.043612 6.129280 6.117566 5.908103 6.069120 5.994529
 [9] 6.162625 6.115662 6.020975 5.920553
> (ub <- forecast$pred + 1.96 * forecast$se)
Time Series:
Start = 175 
End = 186 
Frequency = 1 
 [1] 6.101715 6.076131 6.194079 6.288442 6.279370 6.070318 6.231624 6.157393
 [9] 6.325671 6.278759 6.184103 6.083718
> 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$pred))
  [1] 277.0000 260.6000 291.6000 275.4000 275.3000 231.7000 238.8000 274.2000
  [9] 277.8000 299.1000 286.6000 232.3000 294.1000 267.5000 309.7000 280.7000
 [17] 287.3000 235.7000 256.4000 289.0000 290.8000 321.9000 291.8000 241.4000
 [25] 295.5000 258.2000 306.1000 281.5000 283.1000 237.4000 274.8000 299.3000
 [33] 300.4000 340.9000 318.8000 265.7000 322.7000 281.6000 323.5000 312.6000
 [41] 310.8000 262.8000 273.8000 320.0000 310.3000 342.2000 320.1000 265.6000
 [49] 327.0000 300.7000 346.4000 317.3000 326.2000 270.7000 278.2000 324.6000
 [57] 321.8000 343.5000 354.0000 278.2000 330.2000 307.3000 375.9000 335.3000
 [65] 339.3000 280.3000 293.7000 341.2000 345.1000 368.7000 369.4000 288.4000
 [73] 341.0000 319.1000 374.2000 344.5000 337.3000 281.0000 282.2000 321.0000
 [81] 325.4000 366.3000 380.3000 300.7000 359.3000 327.6000 383.6000 352.4000
 [89] 329.4000 294.5000 333.5000 334.3000 358.0000 396.1000 387.0000 307.2000
 [97] 363.9000 344.7000 397.6000 376.8000 337.1000 299.3000 323.1000 329.1000
[105] 347.0000 462.0000 436.5000 360.4000 415.5000 382.1000 432.2000 424.3000
[113] 386.7000 354.5000 375.8000 368.0000 402.4000 426.5000 433.3000 338.5000
[121] 416.8000 381.1000 445.7000 412.4000 394.0000 348.2000 380.1000 373.7000
[129] 393.6000 434.2000 430.7000 344.5000 411.9000 370.5000 437.3000 411.3000
[137] 385.5000 341.3000 384.2000 373.2000 415.8000 448.6000 454.3000 350.3000
[145] 419.1000 398.0000 456.1000 430.1000 399.8000 362.7000 384.9000 385.3000
[153] 432.3000 468.9000 442.7000 370.2000 439.4000 393.9000 468.7000 438.8000
[161] 430.1000 366.3000 391.0000 380.9000 431.4000 465.4000 471.5000 387.5000
[169] 446.4000 421.5000 504.8000 492.1000 421.3000 396.7000 417.9624 405.5914
[177] 454.3399 497.1347 491.9947 399.0994 468.8919 435.2677 514.9902 491.3766
[185] 446.9914 404.2912
> (perc.se <- (ub-forecast$pred)/1.96/forecast$pred)
Time Series:
Start = 175 
End = 186 
Frequency = 1 
 [1] 0.03498596 0.03742359 0.03986516 0.04226189 0.04299190 0.04310563
 [7] 0.04318591 0.04328555 0.04333593 0.04334999 0.04335845 0.04336868
> postscript(file="/var/www/html/rcomp/tmp/1bj4j1261080761.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> opar <- par(mar=c(4,4,2,2),las=1)
> ylim <- c( min(x[first:nx],lb), max(x[first:nx],ub))
> plot(x,ylim=ylim,type='n',xlim=c(first,lx))
> usr <- par('usr')
> rect(usr[1],usr[3],nx+1,usr[4],border=NA,col='lemonchiffon')
> rect(nx1,usr[3],usr[2],usr[4],border=NA,col='lavender')
> abline(h= (-3:3)*2 , col ='gray', lty =3)
> polygon( c(nx1:lx,lx:nx1), c(lb,rev(ub)), col = 'orange', lty=2,border=NA)
> lines(nx1:lx, lb , lty=2)
> lines(nx1:lx, ub , lty=2)
> lines(x, lwd=2)
> lines(nx1:lx, forecast$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/2gido1261080761.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/392t41261080761.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/4xljm1261080761.tab") 
> 
> try(system("convert tmp/1bj4j1261080761.ps tmp/1bj4j1261080761.png",intern=TRUE))
character(0)
> try(system("convert tmp/2gido1261080761.ps tmp/2gido1261080761.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  3.959   0.347   4.083