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Exponential Smoothing Eigen Reeks Dimitri Troquay

R Software Module: rwasp_exponentialsmoothing.wasp (opens new window with default values)
Title produced by software: Exponential Smoothing
Date of computation: Wed, 28 May 2008 11:41:08 -0600
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/May/28/t12119965418pl9dsca2pyrtwk.htm/, Retrieved Wed, 28 May 2008 19:42:21 +0200
 
User-defined keywords:
 
Dataseries X:
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8,16 8,43 8,43 8,43 8,43 8,43 8,43 8,43 8,43 8,43 8,43 8,43 8,43 8,43 8,68 8,68 8,68 8,68 8,68 8,68 8,68 8,68 8,68 8,68 8,68 8,68 8,92 8,92 8,92 8,92 8,92 8,92 8,92 8,92 8,92 8,92 8,92 8,92 9,3 9,3 9,3 9,3 9,3 9,3 9,3 9,3 9,3 9,3 9,3 9,3 9,6 9,6 9,6 9,6 9,6 9,6 9,6 9,6 9,6 9,6 9,6 9,6 9,9 9,9 9,9 9,9 9,9 9,9 9,9 9,9 9,9 9,9 9,9 9,9 10,2 10,2 10,2 10,2 10,2 10,2 10,2 10,2 10,2 10,2
 
Text written by user:
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001


Estimated Parameters of Exponential Smoothing
ParameterValue
alpha1
beta0.286867200485029
gamma0


Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
38.438.7-0.270000000000000
48.438.62254585586904-0.192545855869042
58.438.5673107652309-0.137310765230897
68.438.52792081041265-0.0979208104126528
78.438.49983054166035-0.06983054166035
88.438.47979844966589-0.0497984496658912
98.438.46551290782174-0.035512907821742
108.438.45532541937384-0.0253254193738357
118.438.44806038721696-0.0180603872169556
128.438.44287945449635-0.0128794544963515
138.438.4391847614412-0.00918476144120817
148.438.43654995463945-0.0065499546394463
158.688.434670987488720.245329012511275
168.688.75504783450559-0.0750478345055914
178.688.7335190723185-0.0535190723185082
188.688.71816620586994-0.0381662058699419
198.688.7072175732389-0.0272175732388966
208.688.69940974419986-0.0194097441998586
218.688.69384172521911-0.0138417252191143
228.688.68987098825562-0.00987098825562427
238.688.68703932548871-0.00703932548871222
248.688.68501997389246-0.00501997389246256
258.688.68357990803542-0.00357990803542307
268.688.6825529498393-0.00255294983930732
278.928.681820592265930.238179407734073
288.928.99014645217578-0.0701464521757824
298.928.97002373581616-0.0500237358161595
308.928.95567356676477-0.0356735667647747
318.928.94543999053565-0.0254399905356486
328.928.93814209167032-0.0181420916703203
338.928.93293772062191-0.0129377206219132
348.928.92922631292645-0.00922631292644738
358.928.92657958636644-0.0065795863664384
368.928.92469211884515-0.0046921188451492
378.928.9233461038477-0.00334610384769896
388.928.92238621640438-0.00238621640437664
399.38.92170168918470.3782983108153
409.39.4102230665565-0.110223066556502
419.39.37860368402456-0.0786036840245625
429.39.35605486524063-0.0560548652406272
439.39.33997456297548-0.0399745629754822
449.39.3285071720041-0.0285071720040939
459.39.32032939937753-0.0203293993775340
469.39.31449756149056-0.0144975614905594
479.39.3103386866119-0.0103386866119024
489.39.30737285652686-0.00737285652685493
499.39.30525782581542-0.00525782581541812
509.39.30374952804311-0.00374952804311057
519.69.302673911430240.297326088569756
529.69.68796701408941-0.0879670140894131
539.69.66273216302256-0.0627321630225559
549.69.6447363630359-0.0447363630359057
559.69.63190296781191-0.0319029678119129
569.69.62275105274855-0.0227510527485464
579.69.61622452193848-0.0162245219384829
589.69.61157023875078-0.0115702387507817
599.69.6082511167514-0.00825111675140278
609.69.60588414198805-0.00588414198805154
619.69.60419617464868-0.00419617464868338
629.69.60299242977447-0.00299242977446923
639.99.602133999822420.297866000177581
649.99.98758198541304-0.0875819854130349
659.99.96245758644468-0.0624575864446779
669.99.94454055347224-0.0445405534722401
679.99.9317633295896-0.0317633295896051
689.99.92265147215215-0.0226514721521518
699.99.916153507749-0.0161535077489994
709.99.91151959620303-0.0115195962030317
719.99.90821500188955-0.00821500188954971
729.99.90585838729552-0.00585838729551469
739.99.9041778081327-0.00417780813269353
749.99.9029793320095-0.00297933200950418
7510.29.902124659376620.297875340623376
7610.210.2875753244348-0.0875753244347752
7710.210.2624528362826-0.0624528362826027
7810.210.2445371659759-0.0445371659758624
7910.210.2317609138548-0.0317609138548303
8010.210.2226497494124-0.022649749412448
8110.210.2161522792068-0.0161522792068123
8210.210.2115187200893-0.0115187200893008
8310.210.2082143771041-0.0082143771041121
8410.210.2058579417405-0.005857941740528


Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
8510.204177490392810.014729481785810.3936254989998
8610.20835498078569.8996054523327410.5171045092385
8710.21253247117859.7833306110137610.6417343313431
8810.21670996157139.661316847627110.7721030755154
8910.22088745196419.532428912349810.9093459915784
9010.22506494235699.396414079371111.0537158053427
9110.22924243274979.2533173057040511.2051675597954
9210.23341992314259.1032938106717111.3635460356134
9310.23759741353548.9465378297439411.5286569973268
9410.24177490392828.7832529202962911.7002968875601
9510.2459523943218.6136391032195311.8782656854225
9610.25012988471388.4378873991972312.0623723702304
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/28/t12119965418pl9dsca2pyrtwk/1k8mj1211996461.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/28/t12119965418pl9dsca2pyrtwk/1k8mj1211996461.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/28/t12119965418pl9dsca2pyrtwk/2nt3t1211996461.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/28/t12119965418pl9dsca2pyrtwk/2nt3t1211996461.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/28/t12119965418pl9dsca2pyrtwk/3j95u1211996461.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/28/t12119965418pl9dsca2pyrtwk/3j95u1211996461.ps (open in new window)


 
Parameters (Session):
par1 = 12 ; par2 = Double ; par3 = additive ;
 
Parameters (R input):
par1 = 12 ; par2 = Double ; par3 = additive ;
 
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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