R version 3.3.2 (2016-10-31) -- "Sincere Pumpkin Patch"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> source('/home/pw/wessanet/cretab')
> 
> 
> 
> myrfcuid = 'r0619023'
> 
> x <- c(1100,680,860,440,1480,1620,1240,1440,1540,860,1180,1180,1480,940,1300,860,1220,2180,940,920,2060,1160,980,1020,740,720,1340,1140,1200,1900,1020,2140,2020,1340,1400,2320,1280,1160,2120,1540,2400,1420,1480,3380,1880,2200,1980,1340,1960,1340,3300,1780,2040,4460,800,1420,1960,1940,1880,940,1880,720,1660,4260,2540,2320,2860,5880,3140,4440,3600,2920,2260,3740,3380,4560,3320,4760,4000,4840,6160,3440,3280,2000,3600,4320,3480,5620,4200,8540,3800,5380,5140,2720,3120,3440,5020,5800,2260,5800,5660,4880,3440,5900,5960,5520,5920,3840)
> par4 = '12'
> par3 = 'additive'
> par2 = 'Double'
> par1 = '12'
> par4 <- '12'
> par3 <- 'additive'
> par2 <- 'Double'
> par1 <- '12'
> #'GNU S' R Code compiled by R2WASP v. 1.2.327 (Sun, 13 Nov 2016 17:42:22 +0100)
> #Author: root
> #To cite this work: Wessa P., (2016), Exponential Smoothing (v1.0.6) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_exponentialsmoothing.wasp/
> #Source of accompanying publication: 
> #
> par1 <- as.numeric(par1)
> par4 <- as.numeric(par4)
> if (par2 == 'Single') K <- 1
> if (par2 == 'Double') K <- 2
> if (par2 == 'Triple') K <- par1
> nx <- length(x)
> nxmK <- nx - K
> x <- ts(x, frequency = par1)
> if (par2 == 'Single') fit <- HoltWinters(x, gamma=F, beta=F)
> if (par2 == 'Double') fit <- HoltWinters(x, gamma=F)
> if (par2 == 'Triple') fit <- HoltWinters(x, seasonal=par3)
> fit
Holt-Winters exponential smoothing with trend and without seasonal component.

Call:
HoltWinters(x = x, gamma = F)

Smoothing parameters:
 alpha: 0.2898885
 beta : 0.1144395
 gamma: FALSE

Coefficients:
        [,1]
a 5163.14173
b   40.17137
> myresid <- x - fit$fitted[,'xhat']
> postscript(file="/home/pw/wessanet/rcomp/tmp/1w76r1481900304.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow=c(2,1))
> plot(fit,ylab='Observed (black) / Fitted (red)',main='Interpolation Fit of Exponential Smoothing')
> plot(myresid,ylab='Residuals',main='Interpolation Prediction Errors')
> par(op)
> dev.off()
null device 
          1 
> postscript(file="/home/pw/wessanet/rcomp/tmp/2jq9k1481900304.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> p <- predict(fit, par4, prediction.interval=TRUE)
> np <- length(p[,1])
> plot(fit,p,ylab='Observed (black) / Fitted (red)',main='Extrapolation Fit of Exponential Smoothing')
> dev.off()
null device 
          1 
> postscript(file="/home/pw/wessanet/rcomp/tmp/3pu4l1481900304.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> acf(as.numeric(myresid),lag.max = nx/2,main='Residual ACF')
> spectrum(myresid,main='Residals Periodogram')
> cpgram(myresid,main='Residal Cumulative Periodogram')
> qqnorm(myresid,main='Residual Normal QQ Plot')
> qqline(myresid)
> par(op)
> dev.off()
null device 
          1 
> 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Estimated Parameters of Exponential Smoothing',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'Value',header=TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'alpha',header=TRUE)
> a<-table.element(a,fit$alpha)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'beta',header=TRUE)
> a<-table.element(a,fit$beta)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'gamma',header=TRUE)
> a<-table.element(a,fit$gamma)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/home/pw/wessanet/rcomp/tmp/4pc8j1481900304.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Interpolation Forecasts of Exponential Smoothing',4,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t',header=TRUE)
> a<-table.element(a,'Observed',header=TRUE)
> a<-table.element(a,'Fitted',header=TRUE)
> a<-table.element(a,'Residuals',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:nxmK) {
+ a<-table.row.start(a)
+ a<-table.element(a,i+K,header=TRUE)
+ a<-table.element(a,x[i+K])
+ a<-table.element(a,fit$fitted[i,'xhat'])
+ a<-table.element(a,myresid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/home/pw/wessanet/rcomp/tmp/51aye1481900304.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Extrapolation Forecasts of Exponential Smoothing',4,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t',header=TRUE)
> a<-table.element(a,'Forecast',header=TRUE)
> a<-table.element(a,'95% Lower Bound',header=TRUE)
> a<-table.element(a,'95% Upper Bound',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:np) {
+ a<-table.row.start(a)
+ a<-table.element(a,nx+i,header=TRUE)
+ a<-table.element(a,p[i,'fit'])
+ a<-table.element(a,p[i,'lwr'])
+ a<-table.element(a,p[i,'upr'])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/home/pw/wessanet/rcomp/tmp/6uuja1481900304.tab") 
> 
> try(system("convert /home/pw/wessanet/rcomp/tmp/1w76r1481900304.ps /home/pw/wessanet/rcomp/tmp/1w76r1481900304.png",intern=TRUE))
character(0)
> try(system("convert /home/pw/wessanet/rcomp/tmp/2jq9k1481900304.ps /home/pw/wessanet/rcomp/tmp/2jq9k1481900304.png",intern=TRUE))
character(0)
> try(system("convert /home/pw/wessanet/rcomp/tmp/3pu4l1481900304.ps /home/pw/wessanet/rcomp/tmp/3pu4l1481900304.png",intern=TRUE))
character(0)
> 
> proc.time()
   user  system elapsed 
  1.364   0.248   1.576