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$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_exponentialsmoothing.wasp/
#Source of accompanying publication: 
#
par1 <- as.numeric(par1)
par4 <- as.numeric(par4)
if (par2 == 'Single') K <- 1
if (par2 == 'Double') K <- 2
if (par2 == 'Triple') K <- par1
nx <- length(x)
nxmK <- nx - K
x <- ts(x, frequency = par1)
if (par2 == 'Single') fit <- HoltWinters(x, gamma=F, beta=F)
if (par2 == 'Double') fit <- HoltWinters(x, gamma=F)
if (par2 == 'Triple') fit <- HoltWinters(x, seasonal=par3)
fit
myresid <- x - fit$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()
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()
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()

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))
try(system("convert /home/pw/wessanet/rcomp/tmp/2jq9k1481900304.ps /home/pw/wessanet/rcomp/tmp/2jq9k1481900304.png",intern=TRUE))
try(system("convert /home/pw/wessanet/rcomp/tmp/3pu4l1481900304.ps /home/pw/wessanet/rcomp/tmp/3pu4l1481900304.png",intern=TRUE))