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opgave 10 oefening 2 P Fieremans

*Unverified author*
R Software Module: /rwasp_exponentialsmoothing.wasp (opens new window with default values)
Title produced by software: Exponential Smoothing
Date of computation: Mon, 31 May 2010 18:02:53 +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/May/31/t127532902476ftibhyo3qa8hr.htm/, Retrieved Mon, 31 May 2010 20:03:48 +0200
 
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/May/31/t127532902476ftibhyo3qa8hr.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:
KDGP2W62
 
Dataseries X:
» Textbox « » Textfile « » CSV «
93,2 96 95,2 77,1 70,9 64,8 70,1 77,3 79,5 100,6 100,7 107,1 95,9 82,8 83,3 80 80,4 67,5 75,7 71,1 89,3 101,1 105,2 114,1 96,3 84,4 91,2 81,9 80,5 70,4 74,8 75,9 86,3 98,7 100,9 113,8 89,8 84,4 87,2 85,6 72 69,2 77,5 78,1 94,3 97,7 100,2 116,4 97,1 93 96 80,5 76,1 69,9 73,6 92,6 94,2 93,5 108,5 109,4 105,1 92,5 97,1 81,4 79,1 72,1 78,7 87,1 91,4 109,9 116,3 113 100 84,8 94,3 87,1 90,3 72,4 84,9 92,7 92,2 114,9 112,5 118,3 106 91,2 96,6 96,3 88,2 70,2 86,5 88,2 102,8 119,1 119,2 125,1 106,1 102,1 105,2 101 84,3 87,5 92,7 94,4 113 113,9 122,9 132,7 106,9 96,6 127,3 98,2 100,2 89,4 95,3 104,2 106,4 116,2 135,9 134 104,6 107,1 123,5 98,8 98,6 90,6 89,1 105,2 114 122,1 138 142,2 116,4 112,6 123,8 103,6 113,9 98,6 95 116 113,9 127,5 131,4 145,9 131,5 131 130,5 118,9 114,3 85,7 104,6 105,1 117,3 142,5 140 159,8 131,2 125,4 126,5 119,4 113,5 98,7 114,5 113, etc...
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'George Udny Yule' @ 72.249.76.132


Estimated Parameters of Exponential Smoothing
ParameterValue
alpha0.0754804609554412
beta0.0688123430653692
gamma0.145742399878318


Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
1395.995.23283596705040.667164032949628
1482.882.41589665822140.384103341778626
1583.382.92469348713670.375306512863276
168079.39341772699140.60658227300857
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1975.770.10546284806335.59453715193669
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348202189.09276910588312.9072308941172
349166.5155.71418540535510.7858145946449
350151.3150.1219096888151.17809031118546
351146.2160.869258858163-14.6692588581630
352148.3142.6903738742235.60962612577745
353144.7138.1393559134266.56064408657414
354123.6127.035205388819-3.43520538881944
355151.6132.44950006682119.1504999331786
356133.9146.492844078083-12.5928440780827
357137.4143.782992727413-6.38299272741304
358181.6161.53637433251720.0636256674827
359182175.5796949426196.42030505738052
360190191.30070031177-1.30070031177007
361161.2156.8364321042264.36356789577425
362155.5149.6349342103595.86506578964148
363141.9158.735187114784-16.8351871147838
364164.6143.29089793220421.3091020677962
365136.2140.230768655027-4.03076865502723
366126.8127.086133693249-0.286133693248814
367152.5135.97828647982516.5217135201749
368126.6145.747217413100-19.1472174131003
369150.1143.5045105936416.5954894063587
370186.3166.24574502787320.0542549721269
371147.5178.820459049232-31.3204590492318
372200.4190.6625632223869.73743677761405
373177.2157.81598934525719.3840106547429
374127.4152.036268159643-24.6362681596433
375177.1155.73046160135721.3695383986428
376154.4148.3395995754856.06040042451454
377135.2140.755226446009-5.55522644600893
378126.4127.998353906702-1.59835390670202
379147.3139.1932255243078.1067744756931
380140.6143.542221776613-2.94222177661291
381152.3146.1418363337236.15816366627732
382151.2171.064457327278-19.8644573272776
383172.2173.555411973667-1.35541197366652
384215.3193.4172701462121.88272985379
385154.1162.496053594127-8.39605359412732
386159.3148.63251344226710.6674865577334
387160.4161.545361181224-1.14536118122368
388151.9150.3581377187041.54186228129578
389148.4140.8381013798867.56189862011433
390139.6129.53814820039810.0618517996021
391148.2143.3397098405594.86029015944146
392153.5146.1465213039147.35347869608623
393145.1151.060318899433-5.96031889943262
394183.7172.15190213462811.5480978653715
395210.5180.08604287942130.4139571205789
396203.3207.238303697958-3.93830369795774
397153.3168.983678191185-15.6836781911848
398144.3156.956345105342-12.6563451053421
399169.6167.0572673517932.54273264820660
400143.7156.303219173420-12.6032191734197
401160.1146.3574323688513.7425676311499
402135.6135.5777666006030.0222333993969244
403141.8148.367365687373-6.56736568737313
404159.9150.7328359493639.16716405063664
405145.7154.082058602173-8.38205860217306
406183.5177.9442184768745.55578152312552
407198.2188.11186350667610.0881364933243
408186.8209.227402537999-22.4274025379993
409172167.5830751324784.41692486752248
410150.6157.254950644472-6.6549506444722
411163.3170.017244445381-6.7172444453808
412153.7156.276884726660-2.57688472665981
413152.9150.5036563959292.39634360407106
414135.5136.801781825397-1.30178182539714
415148.5148.599094607332-0.099094607332006
416148.4153.552637480464-5.15263748046357
417133.6153.310556948823-19.7105569488233
418194.1177.87401285190016.2259871480995
419208.6189.26207405644619.3379259435538
420197.3206.556548526833-9.256548526833
421164.4169.210519559140-4.81051955914026
422148.1156.556086862315-8.45608686231517
423152169.046528016972-17.0465280169716
424144.1154.978579659446-10.8785796594455
425155149.0937394733515.90626052664871
426124.5135.129908403824-10.6299084038238
427153145.9561696452077.04383035479299
428146150.502603038921-4.50260303892091
429138148.152089424049-10.1520894240486
430190177.71265130100512.2873486989951
431192188.8340018410983.16599815890208
432192200.472299877024-8.47229987702445
433147164.368288641132-17.3682886411324
434133150.364193581064-17.3641935810645
435163160.194424867492.80557513250983
436150148.5613652906721.43863470932777
437129145.752440032313-16.7524400323126
438131128.2185840421532.7814159578472
439145141.7395539271893.26044607281077
440137144.104710687596-7.1047106875962
441138140.643121127480-2.64312112747984
442168172.240937376648-4.24093737664813
443176180.094610093677-4.09461009367749
444188188.676334780369-0.676334780369046
445139153.479480209668-14.4794802096682
446143140.0436481589092.95635184109068
447150153.310711924240-3.31071192423963
448154141.38942876131312.6105712386875
449137137.033897157788-0.0338971577880898
450129123.8275139849735.17248601502693
451128137.059923483735-9.05992348373476
452140136.9414615963363.05853840366436
453143134.8667466500318.13325334996932
454151166.007828434087-15.0078284340870
455177172.6802276855434.31977231445731
456184181.9973544081422.00264559185834
457151146.3656846939234.63431530607733
458134137.046300187522-3.04630018752235
459164148.73138091024115.2686190897588
460126140.658736853849-14.6587368538486
461131132.755076320922-1.75507632092206
462125120.5073740176294.49262598237134
463127131.392057650653-4.3920576506533
464143133.1978412510319.80215874896928
465143132.3969120762210.6030879237800
466160159.9832892500160.0167107499840995
467190170.36673412545119.6332658745486
468182180.6938444990381.30615550096203
469138145.880962898054-7.88096289805372
470136134.8566520744211.14334792557946
471152149.3207797692352.67922023076454
472127136.550252906727-9.55025290672728
473151130.94569798219720.0543020178033
474130121.3881446757858.61185532421496
475119131.681550320279-12.6815503202795
476153134.98903969010118.0109603098986


Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
477135.151601073994131.000321391756139.302880756231
478160.847118808214156.282918281663165.411319334765
479174.176650351491169.176766941744179.176533761238
480180.697626543404175.275004310943186.120248775865
481144.718396240073139.460979532342149.975812947803
482135.625274846243130.175441510890141.075108181596
483150.428828785048144.305842627938156.551814942157
484135.882174682901129.740078407166142.024270958637
485135.131309944456128.630480234669141.632139654243
486122.583049334962116.090909171625129.075189498299
487129.334822439710122.212881538455136.456763340964
488137.82725427407290.2473389036252185.407169644518
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/May/31/t127532902476ftibhyo3qa8hr/1i7uj1275328967.png (open in new window)
http://www.freestatistics.org/blog/date/2010/May/31/t127532902476ftibhyo3qa8hr/1i7uj1275328967.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/May/31/t127532902476ftibhyo3qa8hr/2i7uj1275328967.png (open in new window)
http://www.freestatistics.org/blog/date/2010/May/31/t127532902476ftibhyo3qa8hr/2i7uj1275328967.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/May/31/t127532902476ftibhyo3qa8hr/3sztm1275328967.png (open in new window)
http://www.freestatistics.org/blog/date/2010/May/31/t127532902476ftibhyo3qa8hr/3sztm1275328967.ps (open in new window)


 
Parameters (Session):
par1 = 12 ; par2 = Triple ; par3 = multiplicative ;
 
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=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')
 





Copyright

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Software written by Ed van Stee & Patrick Wessa


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