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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: Tue, 28 Dec 2010 15:38:00 +0000
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/28/t1293550551x2otzlg4sq439wz.htm/, Retrieved Tue, 28 Dec 2010 16:35:52 +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/2010/Dec/28/t1293550551x2otzlg4sq439wz.htm/},
    year = {2010},
}
@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 = {2010},
    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 «
11100 8962 9173 8738 8459 8078 8411 8291 7810 8616 8312 9692 9911 8915 9452 9112 8472 8230 8384 8625 8221 8649 8625 10443 10357 8586 8892 8329 8101 7922 8120 7838 7735 8406 8209 9451 10041 9411 10405 8467 8464 8102 7627 7513 7510 8291 8064 9383 9706 8579 9474 8318 8213 8059 9111 7708 7680 8014 8007 8718 9486 9113 9025 8476 7952 7759 7835 7600 7651 8319 8812 8630
 
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'RServer@AstonUniversity' @ vre.aston.ac.uk


Estimated Parameters of Exponential Smoothing
ParameterValue
alpha0.135019238273121
beta0.0440917299700262
gamma0.0333766681540438


Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
1399119855.3944978632555.6055021367483
1489158868.5050503080446.4949496919617
1594529395.4122981142856.5877018857173
1691129059.5608080065352.4391919934733
1784728427.5447052515544.4552947484517
1882308162.7986087795767.2013912204302
1983848503.52373838579-119.523738385786
2086258434.15916403987190.840835960125
2182217985.6692311748235.330768825194
2286498814.04560710458-165.045607104577
2386258488.4642483113136.5357516887
24104439897.66500383773545.334996162268
251035710207.5800348888149.419965111239
2685869240.47675450445-654.476754504447
2788929676.24010234525-784.240102345255
2883299224.94465761887-895.944657618873
2981018457.20578773181-356.205787731811
3079228129.19198695794-207.19198695794
3181208416.01698300128-296.01698300128
3278388319.27103852083-481.271038520834
3377357764.80460021012-29.8046002101173
3484068527.73234758384-121.732347583844
3582098198.8723562186210.1276437813794
3694519584.22169187039-133.221691870385
37100419768.46277445654272.537225543463
3894118772.88139397185638.118606028147
39104059365.226152748241039.77384725176
4084679153.64399041275-686.643990412755
4184648427.65434712236.3456528779898
4281028157.1888010478-55.1888010477978
4376278463.12187198599-836.12187198599
4475138286.0368038243-773.036803824307
4575107701.40708453483-191.407084534827
4682918435.09615523769-144.096155237687
4780648102.12504170216-38.1250417021565
4893839471.63515753307-88.6351575330718
4997069668.6903868375737.3096131624316
5085798645.58256306614-66.582563066142
5194749143.85912583628330.140874163722
5283188771.8804255021-453.880425502099
5382138084.83642706802128.163572931981
5480597811.31731168935247.682688310645
5591118124.59311036594986.406889634055
5677088195.25062554122-487.250625541223
5776807667.5455031886312.4544968113705
5880148432.8855319782-418.885531978196
5980078066.99593374934-59.9959337493392
6087189433.08858293744-715.088582937438
6194869546.4609336245-60.4609336244976
6291138503.83510699378609.164893006217
6390259105.50922047261-80.5092204726134
6484768653.71049770796-177.710497707958
6579528020.66304206782-68.6630420678202
6677597722.7519074008436.2480925991576
6778358026.28116891763-191.281168917627
6876007885.84644121834-285.846441218339
6976517391.42339227517259.576607724832
7083198170.81003207372148.189967926277
7188127888.35690790335923.643092096655
7286309370.71210645267-740.712106452673


Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
739501.736458417648679.8222321994510323.6506846358
748489.17862886397659.13753257869319.2197251492
758987.638642275328148.85900642949826.41827812125
768543.329177917977695.190010671079391.46834516488
777937.907412067517079.780140711888796.03468342315
787653.187813485386784.43798655928521.93764041155
797945.930183605137065.919118541368825.9412486689
807830.406826969756938.493189787348722.32046415216
817393.841565618166489.382871009868298.30026022646
828136.935857479237219.289879657249054.58183530122
837857.950735333126926.47682208098789.42464858533
849163.136489039838217.1967735090110109.0762045706
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293550551x2otzlg4sq439wz/13t0d1293550676.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293550551x2otzlg4sq439wz/13t0d1293550676.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t1293550551x2otzlg4sq439wz/2w3zy1293550676.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293550551x2otzlg4sq439wz/2w3zy1293550676.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t1293550551x2otzlg4sq439wz/3w3zy1293550676.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293550551x2otzlg4sq439wz/3w3zy1293550676.ps (open in new window)


 
Parameters (Session):
par1 = 12 ; par2 = Triple ; par3 = additive ;
 
Parameters (R input):
par1 = 12 ; par2 = Triple ; 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=F, beta=F)
if (par2 == 'Double') fit <- HoltWinters(x, gamma=F)
if (par2 == 'Triple') fit <- HoltWinters(x, seasonal=par3)
fit
myresid <- x - fit$fitted[,'xhat']
bitmap(file='test1.png')
op <- par(mfrow=c(2,1))
plot(fit,ylab='Observed (black) / Fitted (red)',main='Interpolation Fit of Exponential Smoothing')
plot(myresid,ylab='Residuals',main='Interpolation Prediction Errors')
par(op)
dev.off()
bitmap(file='test2.png')
p <- predict(fit, par1, prediction.interval=TRUE)
np <- length(p[,1])
plot(fit,p,ylab='Observed (black) / Fitted (red)',main='Extrapolation Fit of Exponential Smoothing')
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(myresid),lag.max = nx/2,main='Residual ACF')
spectrum(myresid,main='Residals Periodogram')
cpgram(myresid,main='Residal Cumulative Periodogram')
qqnorm(myresid,main='Residual Normal QQ Plot')
qqline(myresid)
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Estimated Parameters of Exponential Smoothing',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'Value',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'alpha',header=TRUE)
a<-table.element(a,fit$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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