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*The author of this computation has been verified*
R Software Module: /rwasp_exponentialsmoothing.wasp (opens new window with default values)
Title produced by software: Exponential Smoothing
Date of computation: Fri, 18 Dec 2009 02:48:55 -0700
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/18/t12611297738huqowwaz9exbqr.htm/, Retrieved Fri, 18 Dec 2009 10:49:38 +0100
 
BibTeX entries for LaTeX users:
@Manual{KEY,
    author = {{YOUR NAME}},
    publisher = {Office for Research Development and Education},
    title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2009/Dec/18/t12611297738huqowwaz9exbqr.htm/},
    year = {2009},
}
@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Development Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2009},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}
 
Original text written by user:
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
20366 22782 19169 13807 29743 25591 29096 26482 22405 27044 17970 18730 19684 19785 18479 10698 31956 29506 34506 27165 26736 23691 18157 17328 18205 20995 17382 9367 31124 26551 30651 25859 25100 25778 20418 18688 20424 24776 19814 12738 31566 30111 30019 31934 25826 26835 20205 17789 20520 22518 15572 11509 25447 24090 27786 26195 20516 22759 19028 16971
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Estimated Parameters of Exponential Smoothing
ParameterValue
alpha0.265104594525292
beta0.0126902695364225
gamma0.68717139456287


Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
131968419339.8654646862344.134535313824
141978519396.4130987381388.586901261864
151847918124.4957673802354.504232619849
161069810572.9582222105125.041777789471
173195631995.1982638565-39.1982638565387
182950629720.8574988809-214.857498880861
193450629970.35584201314535.64415798687
202716528692.2494763723-1527.24947637234
212673624200.54751307412535.4524869259
222369130373.0773557657-6682.07735576571
231815719113.1663297864-956.166329786385
241732819509.3961197037-2181.39611970365
251820519803.1752069484-1598.17520694841
262099519351.64019730161643.35980269840
271738218376.8009260429-994.800926042932
28936710463.4408064360-1096.44080643596
293112430455.379667643668.620332356975
302655128349.8478283896-1798.84782838963
313065130306.9608789443344.039121055735
322585925317.5997310609541.400268939062
332510023516.88126488901583.11873511095
342577824487.70688829721290.29311170276
352041818318.70932731052099.29067268951
361868818867.8912229154-179.891222915434
372042420033.08152779390.918472210011
382477621867.00666518912908.99333481086
391981419625.4192282421188.580771757937
401273811059.46213306221678.53786693783
413156636866.8705042877-5300.8705042877
423011131425.3228958095-1314.32289580948
433001935155.5244637262-5136.52446372624
443193428296.78937771863637.21062228137
452582627649.7084603273-1823.70846032732
462683527607.2036568489-772.203656848902
472020520808.1006632482-603.100663248188
481778919443.2014206495-1654.20142064947
492052020517.63959995622.36040004378447
502251823485.3185822666-967.318582266555
511557218984.7634727226-3412.76347272255
521150910843.7384176495665.2615823505
532544730360.7380569884-4913.73805698839
542409027212.871033591-3122.871033591
552778628104.9927413588-318.992741358776
562619526966.4903567541-771.490356754064
572051622943.5300978850-2427.53009788503
582275923075.3574231874-316.357423187404
591902817413.83015637361614.16984362635
601697116274.9834314474696.016568552579


Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
6118562.990156089715188.526041202921937.4542709765
6220771.483945307317150.217370294224392.7505203204
6315668.828254446112039.044765355219298.6117435370
6410696.59203140137107.595949380614285.5881134220
6526132.121283675620772.717596400331491.5249709509
6625147.229798746919756.535203178230537.9243943157
6728322.377955588622320.339014504534324.4168966727
6827013.413718994121053.533885037532973.2935529507
6922209.351521844716827.228816571427591.4742271181
7024157.120282523218305.560762011630008.6798030348
7119273.381086549014067.0671409424479.6950321581
7217173.25558968713454.571840927120891.9393384469
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2009/Dec/18/t12611297738huqowwaz9exbqr/1tkeg1261129734.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/18/t12611297738huqowwaz9exbqr/1tkeg1261129734.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/18/t12611297738huqowwaz9exbqr/2t1jl1261129734.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/18/t12611297738huqowwaz9exbqr/2t1jl1261129734.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/18/t12611297738huqowwaz9exbqr/37ms21261129734.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/18/t12611297738huqowwaz9exbqr/37ms21261129734.ps (open in new window)


 
Parameters (Session):
par1 = FALSE ; par2 = 0.5 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
 
Parameters (R input):
par1 = 12 ; par2 = Triple ; par3 = multiplicative ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
 
R code (references can be found in the software module):
par1 <- as.numeric(par1)
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=0, beta=0)
if (par2 == 'Double') fit <- HoltWinters(x, gamma=0)
if (par2 == 'Triple') fit <- HoltWinters(x, seasonal=par3)
fit
myresid <- x - fit$fitted[,'xhat']
bitmap(file='test1.png')
op <- par(mfrow=c(2,1))
plot(fit,ylab='Observed (black) / Fitted (red)',main='Interpolation Fit of Exponential Smoothing')
plot(myresid,ylab='Residuals',main='Interpolation Prediction Errors')
par(op)
dev.off()
bitmap(file='test2.png')
p <- predict(fit, par1, prediction.interval=TRUE)
np <- length(p[,1])
plot(fit,p,ylab='Observed (black) / Fitted (red)',main='Extrapolation Fit of Exponential Smoothing')
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(myresid),lag.max = nx/2,main='Residual ACF')
spectrum(myresid,main='Residals Periodogram')
cpgram(myresid,main='Residal Cumulative Periodogram')
qqnorm(myresid,main='Residual Normal QQ Plot')
qqline(myresid)
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Estimated Parameters of Exponential Smoothing',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'Value',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'alpha',header=TRUE)
a<-table.element(a,fit$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='mytable.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='mytable1.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='mytable2.tab')
 





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