Gemiddelde consumptieprijs van rode tafelwijn

*Unverified author*

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

Title produced by software: Exponential Smoothing

Date of computation: Thu, 14 Jan 2010 12:07:55 -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/14/t1263496111w7ezi9tz20sm2zc.htm/, Retrieved Thu, 14 Jan 2010 20:08:36 +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/14/t1263496111w7ezi9tz20sm2zc.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 2 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Estimated Parameters of Exponential Smoothing Parameter Value alpha 0.88008275007892 beta 0.141425389189999 gamma 1 Interpolation Forecasts of Exponential Smoothing t Observed Fitted Residuals 13 2.17 2.15729700854701 0.0127029914529908 14 2.18 2.18086489286783 -0.000864892867825873 15 2.18 2.18280093311450 -0.00280093311449692 16 2.18 2.18268447666828 -0.00268447666828431 17 2.17 2.17233638533652 -0.00233638533651703 18 2.17 2.17242050920447 -0.00242050920447445 19 2.18 2.18671265923205 -0.006712659232051 20 2.17 2.17722519724388 -0.00722519724387816 21 2.18 2.17680403433087 0.00319596566913338 22 2.17 2.16678547967492 0.00321452032507885 23 2.17 2.16801668661782 0.00198331338218116 24 2.17 2.17924451807540 -0.00924451807539528 25 2.17 2.16138027460692 0.00861972539308375 26 2.17 2.17757724228165 -0.00757724228164536 27 2.17 2.1703879541396 -0.000387954139601021 28 2.17 2.16972367711803 0.000276322881972657 29 2.17 2.15970618867926 0.0102938113207376 30 2.17 2.17015098561080 -0.000150985610804621 31 2.18 2.18546342276160 -0.00546342276159795 32 2.18 2.17670703900138 0.00329296099862342 33 2.18 2.18779466538795 -0.00779466538794837 34 2.18 2.16773997303508 0.0122600269649209 35 2.18 2.17754449191288 0.00245550808712158 36 2.18 2.18866041584772 -0.0086604158477157 37 2.18 2.17434409525091 0.00565590474908717 38 2.18 2.18651309827509 -0.00651309827509117 39 2.18 2.18177765296916 -0.00177765296915622 40 2.18 2.18045220232528 -0.000452202325275319 41 2.18 2.17138636250353 0.00861363749647337 42 2.18 2.17928237293983 0.000717627060168091 43 2.18 2.19501273787818 -0.0150127378781764 44 2.19 2.17800417219209 0.0119958278079140 45 2.19 2.19560661940866 -0.00560661940866325 46 2.19 2.18034000510316 0.009659994896841 47 2.2 2.18681444692766 0.0131855530723439 48 2.2 2.20751013085986 -0.00751013085985663 49 2.21 2.19753552498397 0.0124644750160252 50 2.21 2.21669739064261 -0.00669739064260844 51 2.21 2.21480470700586 -0.00480470700586277 52 2.2 2.21303446989610 -0.0130344698961022 53 2.21 2.19447660609998 0.0155233939000228 54 2.2 2.20886119824393 -0.00886119824392839 55 2.21 2.21443711580331 -0.00443711580331074 56 2.21 2.21145312509673 -0.00145312509673046 57 2.22 2.21491296530842 0.00508703469158345 58 2.22 2.21202380026744 0.00797619973256358 59 2.23 2.21836498128105 0.01163501871895 60 2.24 2.23594715132076 0.0040528486792426 61 2.24 2.24071627656621 -0.00071627656620743 62 2.25 2.24651164835017 0.00348835164982564 63 2.25 2.25560950174062 -0.00560950174061547 64 2.32 2.25384319403829 0.066156805961711 65 2.36 2.31996051741567 0.04003948258433 66 2.37 2.36760431453241 0.00239568546759283 67 2.37 2.39962599631797 -0.0296259963179746 68 2.37 2.38770462924133 -0.0177046292413348 69 2.38 2.38849640942027 -0.00849640942026886 70 2.38 2.38315880313578 -0.00315880313577521 71 2.41 2.38791273882031 0.0220872611796916 72 2.42 2.42285918656155 -0.00285918656154527 73 2.43 2.42918760713903 0.00081239286096757 74 2.44 2.44523716810679 -0.00523716810679264 75 2.44 2.45288344771435 -0.0128834477143540 76 2.44 2.45973471980118 -0.0197347198011837 77 2.43 2.44285113274165 -0.0128511327416478 78 2.43 2.42857224267078 0.00142775732921807 79 2.43 2.44492121302189 -0.0149212130218905 80 2.42 2.43822019328998 -0.0182201932899790 81 2.42 2.43044763228951 -0.0104476322895062 82 2.42 2.41457517193747 0.00542482806253375 83 2.42 2.42152153506199 -0.00152153506199193 84 2.42 2.42137096865162 -0.00137096865162478 Extrapolation Forecasts of Exponential Smoothing t Forecast 95% Lower Bound 95% Upper Bound 85 2.41830685202448 2.3927560780378 2.44385762601116 86 2.42167230018505 2.38545577217708 2.45788882819302 87 2.42241895673412 2.37611779018755 2.46872012328069 88 2.43079885145166 2.37450006983472 2.48709763306861 89 2.42557692247115 2.35917565247378 2.49197819246852 90 2.41938791832089 2.34268881172194 2.49608702491984 91 2.42740965364956 2.34017059650561 2.51464871079351 92 2.43019194898317 2.33214575341954 2.5282381445468 93 2.43840154286245 2.32926772379222 2.54753536193269 94 2.43394243365998 2.3134340004082 2.55445086691176 95 2.43492149192300 2.30274902914536 2.56709395470063 96 2.43595741873769 2.29183160511311 2.58008323236226

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jan/14/t1263496111w7ezi9tz20sm2zc/19l0p1263496073.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/14/t1263496111w7ezi9tz20sm2zc/19l0p1263496073.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/14/t1263496111w7ezi9tz20sm2zc/2dom31263496073.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/14/t1263496111w7ezi9tz20sm2zc/2dom31263496073.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/14/t1263496111w7ezi9tz20sm2zc/3emgo1263496073.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/14/t1263496111w7ezi9tz20sm2zc/3emgo1263496073.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=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')

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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