consumptieprijs rode wijn

*Unverified author*

R Software Module: /rwasp_exponentialsmoothing.wasp (opens new window with default values)

Title produced by software: Exponential Smoothing

Date of computation: Tue, 26 Jan 2010 05:42:30 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Jan/26/t1264509794yorethu7fhcwsw0.htm/, Retrieved Tue, 26 Jan 2010 13:43:19 +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/Jan/26/t1264509794yorethu7fhcwsw0.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 «

2.12 2.13 2.14 2.15 2.15 2.16 2.17 2.17 2.18 2.17 2.17 2.18 2.17 2.18 2.18 2.18 2.17 2.17 2.18 2.17 2.18 2.17 2.17 2.17 2.17 2.17 2.17 2.17 2.17 2.17 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.19 2.19 2.19 2.2 2.2 2.21 2.21 2.21 2.2 2.21 2.2 2.21 2.21 2.22 2.22 2.23 2.24 2.24 2.25 2.25 2.32 2.36 2.37 2.37 2.37 2.38 2.38 2.41 2.42 2.43 2.44 2.44 2.44 2.43 2.43 2.43 2.42 2.42 2.42 2.42 2.42

Output produced by software:

Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 1 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Estimated Parameters of Exponential Smoothing Parameter Value alpha 1 beta 0.191978877192319 gamma FALSE Interpolation Forecasts of Exponential Smoothing t Observed Fitted Residuals 3 2.14 2.14 4.44089209850063e-16 4 2.15 2.15 0 5 2.15 2.16 -0.00999999999999979 6 2.16 2.15808021122808 0.00191978877192334 7 2.17 2.16844877012096 0.00155122987904299 8 2.17 2.17874657349140 -0.00874657349140273 9 2.18 2.17706741613324 0.0029325838667571 10 2.17 2.18763041029126 -0.0176304102912557 11 2.17 2.1742457439191 -0.00424574391910015 12 2.18 2.17343065076867 0.00656934923133479 13 2.17 2.18469182705798 -0.0146918270579817 14 2.18 2.17187130659549 0.00812869340451394 15 2.18 2.18343184402833 -0.00343184402832541 16 2.18 2.18277300246507 -0.00277300246506851 17 2.17 2.18224064456537 -0.0122406445653733 18 2.17 2.16989069936560 0.000109300634397513 19 2.18 2.16991168277867 0.0100883172213297 20 2.17 2.18184842659158 -0.0118484265915817 21 2.18 2.16957377895803 0.0104262210419663 22 2.17 2.18157539316703 -0.0115753931670297 23 2.17 2.16935316218376 0.000646837816236534 24 2.17 2.16947734138145 0.000522658618550054 25 2.17 2.16957768079619 0.000422319203805976 26 2.17 2.16965875716276 0.000341242837242461 27 2.17 2.1697242685795 0.000275731420498637 28 2.17 2.16977720318802 0.000222796811984782 29 2.17 2.16981997546982 0.000180024530177736 30 2.17 2.16985453637699 0.000145463623007380 31 2.18 2.16988246232001 0.0101175376799900 32 2.18 2.18182481584377 -0.00182481584376548 33 2.18 2.18147448974700 -0.00147448974699671 34 2.18 2.18119141886094 -0.00119141886093654 35 2.18 2.18096269160575 -0.000962691605748134 36 2.18 2.18077787515219 -0.000777875152194074 37 2.18 2.18062853955388 -0.000628539553880092 38 2.18 2.18050787323606 -0.000507873236055278 39 2.18 2.18041037230244 -0.000410372302441342 40 2.18 2.18033158948859 -0.000331589488587802 41 2.18 2.18026793131088 -0.000267931310879987 42 2.18 2.18021649415865 -0.000216494158652658 43 2.18 2.18017493185316 -0.000174931853155869 44 2.19 2.1801413486324 0.009858651367598 45 2.19 2.19203400145258 -0.00203400145258392 46 2.19 2.19164351613751 -0.00164351613750924 47 2.2 2.19132799575478 0.00867200424521775 48 2.2 2.20299283739279 -0.00299283739278655 49 2.21 2.2024182758305 0.00758172416949954 50 2.21 2.21387380672374 -0.00387380672374249 51 2.21 2.21313011765846 -0.00313011765845861 52 2.2 2.21252920118491 -0.0125292011849076 53 2.21 2.20012385920931 0.00987614079068733 54 2.2 2.21201986962930 -0.0120198696293019 55 2.21 2.19971230855387 0.0102876914461292 56 2.21 2.2116873280066 -0.00168732800659965 57 2.22 2.21136339667044 0.00863660332956284 58 2.22 2.22302144208040 -0.00302144208040245 59 2.23 2.22244138902231 0.00755861097769461 60 2.24 2.23389248267094 0.00610751732906367 61 2.24 2.24506499699020 -0.0050649969902028 62 2.25 2.24409262455504 0.00590737544495878 63 2.25 2.25522671586012 -0.0052267158601178 64 2.32 2.25422329681789 0.0657767031821108 65 2.36 2.33685103444020 0.0231489655597970 66 2.37 2.38129514685654 -0.0112951468565363 67 2.37 2.38912671724530 -0.0191267172452965 68 2.37 2.38545479154417 -0.0154547915441694 69 2.38 2.38248779801628 -0.00248779801627874 70 2.38 2.39201019334643 -0.0120101933464318 71 2.41 2.38970448991292 0.0202955100870787 72 2.42 2.42360079915148 -0.00360079915148415 73 2.43 2.43290952177339 -0.00290952177338699 74 2.44 2.44235095505017 -0.00235095505016591 75 2.44 2.45189962133931 -0.0118996213393054 76 2.44 2.44961514539557 -0.00961514539557173 77 2.43 2.44776924057849 -0.0177692405784886 78 2.43 2.43435792172367 -0.00435792172367044 79 2.43 2.43352129280427 -0.00352129280426805 80 2.42 2.43284527896544 -0.0128452789654396 81 2.42 2.42037925673243 -0.000379256732432331 82 2.42 2.42030644745077 -0.000306447450772129 83 2.42 2.42024761601325 -0.000247616013254515 84 2.42 2.42020007896906 -0.000200078969055273 Extrapolation Forecasts of Exponential Smoothing t Forecast 95% Lower Bound 95% Upper Bound 85 2.42016166803323 2.39887428024957 2.44144905581688 86 2.42032333606645 2.38720236580123 2.45344430633167 87 2.42048500409968 2.3761573594225 2.46481264877686 88 2.42064667213290 2.36505548742478 2.47623785684103 89 2.42080834016613 2.35367622728531 2.48794045304695 90 2.42097000819936 2.34192973318055 2.50001028321816 91 2.42113167623258 2.32977678579139 2.51248656667377 92 2.42129334426581 2.31720119239107 2.52538549614055 93 2.42145501229903 2.30419825128766 2.53871177331041 94 2.42161668033226 2.2907693094184 2.55246405124612 95 2.42177834836549 2.27691897447779 2.56663772225318 96 2.42194001639871 2.26265360174972 2.58122643104770

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jan/26/t1264509794yorethu7fhcwsw0/1rfbc1264509749.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/26/t1264509794yorethu7fhcwsw0/1rfbc1264509749.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/26/t1264509794yorethu7fhcwsw0/2nj8y1264509749.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/26/t1264509794yorethu7fhcwsw0/2nj8y1264509749.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/26/t1264509794yorethu7fhcwsw0/3vvz21264509749.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/26/t1264509794yorethu7fhcwsw0/3vvz21264509749.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=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

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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