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exponential smoothing - Bisocoop - Evy Heynen

R Software Module: rwasp_exponentialsmoothing.wasp (opens new window with default values)
Title produced by software: Exponential Smoothing
Date of computation: Tue, 27 May 2008 06:03:46 -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/27/t1211889904lzvqkz7bn9spdy7.htm/, Retrieved Tue, 27 May 2008 14:05:04 +0200
 
User-defined keywords:
exponential smoothing - Bisocoop - Evy Heynen
 
Dataseries X:
» Textbox « » Textfile « » CSV «
5,44 5,44 5,44 5,44 5,49 5,49 5,49 5,49 5,49 5,49 5,6 5,6 5,6 5,6 5,6 5,6 5,6 5,67 5,67 5,67 5,67 5,67 5,67 5,67 5,67 5,67 5,82 5,82 5,95 5,95 5,95 5,95 5,95 5,95 6,02 6,02 6,05 6,05 6,05 6,12 6,12 6,12 6,12 6,12 6,12 6,12 6,12 6,17 6,17 6,17 6,17 6,17 6,28 6,27 6,28 6,28 6,27 6,27 6,28 6,59 6,59 6,59 6,59 6,59 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,79 6,79 6,79
 
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 time3 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001


Estimated Parameters of Exponential Smoothing
ParameterValue
alpha0.91195083019207
beta0.0325127367673738
gamma0


Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
35.445.440
45.445.440
55.495.440.0499999999999998
65.495.487080042373940.00291995762605524
75.495.49131197781358-0.00131197781358239
85.495.49164569605087-0.00164569605087017
95.495.49162628474826-0.00162628474825599
105.495.49157635622828-0.00157635622827712
115.65.491525221074120.108474778925875
125.65.595051589057720.0049484109422826
135.65.60431372028188-0.00431372028188459
145.65.6050013413659-0.00500134136589558
155.65.60491359597353-0.00491359597352936
165.65.60476018185902-0.00476018185902127
175.65.60460553439919-0.00460553439919398
185.675.604455363644190.065544636355809
195.675.67022209894834-0.000222098948337468
205.675.67600622015519-0.00600622015518759
215.675.67633742269409-0.00633742269408888
225.675.67617868011024-0.0061786801102448
235.675.67598150498544-0.00598150498544481
245.675.67578679215318-0.00578679215318001
255.675.67559806936257-0.00559806936257168
265.675.67541546962416-0.00541546962416195
275.825.675238823100820.144761176899185
285.825.816308065447420.00369193455258365
295.955.828838561044770.121161438955226
305.955.95208790746389-0.00208790746389109
315.955.96287800360307-0.0128780036030669
325.955.96344622958082-0.0134462295808184
335.955.96309758046691-0.0130975804669085
345.955.96267853871462-0.0126785387146153
356.025.962265723544150.0577342764558484
366.026.02777775591951-0.00777775591951091
376.056.033315425385130.0166845746148745
386.056.06165623541576-0.0116562354157601
396.056.06380601262907-0.0138060126290656
406.126.063585950214850.056414049785146
416.126.12907580956677-0.00907580956677112
426.126.134573039403-0.0145730394029986
436.126.1346249750561-0.0146249750561012
446.126.13419591718401-0.0141959171840087
456.126.13373722980466-0.0137372298046632
466.126.13328953366053-0.0132895336605294
476.126.13285607948394-0.0128560794839432
486.176.13243673122460.0375632687753944
496.176.17911110103665-0.00911110103665358
506.176.18295059624667-0.0129505962466743
516.176.18290467520981-0.0129046752098096
526.176.18251800805758-0.0125180080575849
536.286.182112803180540.0978871968194639
546.276.28529407362649-0.0152940736264933
556.286.28480612097965-0.00480612097965416
566.286.29374016388599-0.0137401638859860
576.276.29411940285017-0.0241194028501743
586.276.28431814551275-0.0143181455127532
596.286.28303061967915-0.00303061967915053
606.596.291946904474440.298053095525558
616.596.58427401274270.00572598725729812
626.596.61018294755924-0.0201829475592383
636.596.61186578301641-0.0218657830164117
646.596.61136563443691-0.0213656344369131
656.636.610688105339310.0193118946606905
666.636.64767908068296-0.0176790806829583
676.636.65041192030449-0.020411920304487
686.636.6500473307745-0.0200473307744948
696.636.64942082526504-0.0194208252650405
706.636.64878983417022-0.0187898341702208
716.636.6481771570202-0.0181771570202010
726.636.64758425828591-0.0175842582859076
736.636.64701068048242-0.0170106804824197
746.636.64645581046264-0.016455810462638
756.636.64591903955336-0.0159190395533590
766.636.64539977752247-0.0153997775224735
776.636.64489745326199-0.0148974532619865
786.636.64441151428113-0.0144115142811287
796.636.64394142610972-0.0139414261097217
806.636.64348667171134-0.0134866717113376
816.636.64304675091471-0.0130467509147101
826.796.642621179863630.147378820136373
836.796.79286562608137-0.00286562608137153
846.796.80600955897625-0.0160095589762461


Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
856.806692187655256.711671660303426.90171271500708
866.821974746933656.691460250951776.95248924291554
876.837257306212056.677409720645166.99710489177895
886.852539865490456.666538718970927.03854101200999
896.867822424768866.65761910323397.07802574630381
906.883104984047266.65000810443997.11620186365461
916.898387543325666.643321947371347.15345313927997
926.913670102604066.63731124117077.19002896403742
936.928952661882466.631804018136267.22610130562866
946.944235221160866.62667637332357.26179406899823
956.959517780439266.621835960660077.29719960021845
966.974800339717666.617212084559787.33238859487555
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/27/t1211889904lzvqkz7bn9spdy7/16gjk1211889819.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/27/t1211889904lzvqkz7bn9spdy7/16gjk1211889819.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/27/t1211889904lzvqkz7bn9spdy7/29rrr1211889819.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/27/t1211889904lzvqkz7bn9spdy7/29rrr1211889819.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/27/t1211889904lzvqkz7bn9spdy7/3gxti1211889819.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/27/t1211889904lzvqkz7bn9spdy7/3gxti1211889819.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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