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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, 04 Dec 2009 12:58:01 -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/04/t12599568134erkuy70zl7ckxc.htm/, Retrieved Fri, 04 Dec 2009 21:00:17 +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/04/t12599568134erkuy70zl7ckxc.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 «
6802.96 7132.68 7073.29 7264.5 7105.33 7218.71 7225.72 7354.25 7745.46 8070.26 8366.33 8667.51 8854.34 9218.1 9332.9 9358.31 9248.66 9401.2 9652.04 9957.38 10110.63 10169.26 10343.78 10750.21 11337.5 11786.96 12083.04 12007.74 11745.93 11051.51 11445.9 11924.88 12247.63 12690.91 12910.7 13202.12 13654.67 13862.82 13523.93 14211.17 14510.35 14289.23 14111.82 13086.59 13351.54 13747.69 12855.61 12926.93 12121.95 11731.65 11639.51 12163.78 12029.53 11234.18 9852.13 9709.04 9332.75 7108.6 6691.49 6143.05
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Estimated Parameters of Exponential Smoothing
ParameterValue
alpha1
beta0.176362058368753
gamma0.479537072882266


Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
138854.347758.35416115461095.98583884539
149218.19392.10719616172-174.007196161721
159332.99477.31508899954-144.415088999538
169358.319496.38394000953-138.073940009532
179248.669374.8502648369-126.190264836894
189401.29505.65425610844-104.454256108442
199652.049399.20384066522252.836159334785
209957.389893.595776908963.7842230911028
2110110.6310556.0337836706-445.403783670636
2210169.2610533.1392441504-363.879244150394
2310343.7810490.1606506331-146.380650633082
2410750.2110636.6790413784113.530958621624
2511337.510906.8953002396430.604699760373
2611786.9611772.896021901514.0639780984493
2712083.0411909.8761193753173.163880624727
2812007.7412146.5864978091-138.846497809071
2911745.9311895.4333983305-149.503398330531
3011051.5111944.6936977710-893.183697771025
3111445.910817.4201879184628.479812081601
3211924.8811556.8932393684367.986760631593
3312247.6312510.6936107980-263.063610797968
3412690.9112671.214470795419.6955292045677
3512910.713077.9663608808-167.266360880754
3613202.1213265.4076767090-63.2876767089838
3713654.6713350.9388804778303.731119522243
3813862.8214100.2577307416-237.437730741571
3913523.9313892.0469578098-368.116957809822
4014211.1713398.1988744156812.97112558436
4114510.3514037.415506651472.934493349001
4214289.2314818.4073565848-529.177356584833
4314111.8214142.8103012448-30.9903012448431
4413086.5914266.0795960327-1179.48959603274
4513351.5413483.1778420786-131.637842078622
4613747.6913592.0466794586155.643320541370
4712855.6113967.8734777699-1112.26347776991
4812926.9312855.671356074971.2586439250663
4912121.9512746.2478767205-624.297876720511
5011731.6512040.6082042189-308.958204218876
5111639.5111274.7136004484364.796399551631
5212163.7811173.6319913155990.148008684493
5312029.5311718.8152167479310.714783252055
5411234.1811981.3512041393-747.171204139322
559852.1310780.2956805676-928.165680567628
569709.049483.63219436466225.407805635339
579332.759702.91720775136-370.167207751361
587108.69145.57380846076-2036.97380846076
596691.496481.45123315786210.038766842144
606143.056081.1901041726561.8598958273524


Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
615444.311845337354353.18666978286535.4370208919
624830.622760200133129.755896562946531.48962383733
634083.229940986171845.618662962886320.84121900947
643328.18624262841578.8404345638966077.53205069293
652566.16528062199-656.7079155187065789.03847676269
661899.64364108859-1887.906806827545687.19408900472
671204.22777904199-3062.427476814255470.88303489822
68556.003241732614-4285.921953714085397.92843717931
69-88.9399258571853-5699.245211238265521.36535952389
70-764.110964473387-7134.186203294725605.96427434794
71-1458.65852935581-8603.55646702285686.23940831118
72-2171.96421143836-10049.60160707795705.6731842012
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2009/Dec/04/t12599568134erkuy70zl7ckxc/1758j1259956679.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/04/t12599568134erkuy70zl7ckxc/1758j1259956679.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/04/t12599568134erkuy70zl7ckxc/2vexz1259956679.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/04/t12599568134erkuy70zl7ckxc/2vexz1259956679.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/04/t12599568134erkuy70zl7ckxc/3wag51259956679.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/04/t12599568134erkuy70zl7ckxc/3wag51259956679.ps (open in new window)


 
Parameters (Session):
par1 = Aandelenkoers ; par2 = belgostat ; par3 = euronext brussel ;
 
Parameters (R input):
par1 = 12 ; par2 = Triple ; par3 = multiplicative ;
 
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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