Opgave 10-Stap 2- Gemiddelde prijs pepersteak-Stap 4 verbetering- Van Lishout Silke

*Unverified author*

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

Title produced by software: Exponential Smoothing

Date of computation: Thu, 19 May 2011 18:13:11 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2011/May/19/t1305828716jh9ppu2hwmljk4z.htm/, Retrieved Thu, 19 May 2011 20:11:56 +0200

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

KDGP2W102

Dataseries X:

» Textbox « » Textfile « » CSV «

13,81 13,9 13,91 13,94 13,96 14,01 14,01 14,06 14,09 14,13 14,12 14,13 14,14 14,16 14,21 14,26 14,29 14,32 14,33 14,39 14,48 14,44 14,46 14,48 14,53 14,58 14,62 14,62 14,61 14,65 14,68 14,7 14,78 14,84 14,89 14,89 15,13 15,25 15,33 15,36 15,4 15,4 15,41 15,47 15,54 15,55 15,59 15,65 15,75 15,86 15,89 15,94 15,93 15,95 15,99 15,99 16,06 16,08 16,07 16,11 16,15 16,18 16,3 16,42 16,49 16,5 16,58 16,64 16,66 16,81 16,91 16,92 16,95 17,11 17,16 17,16 17,27 17,34 17,39 17,43 17,45 17,5 17,56 17,65

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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Estimated Parameters of Exponential Smoothing Parameter Value alpha 1 beta 0.165143187979721 gamma FALSE Interpolation Forecasts of Exponential Smoothing t Observed Fitted Residuals 3 13.91 13.99 -0.08 4 13.94 13.9867885449616 -0.0467885449616237 5 13.96 14.0090617354857 -0.0490617354857257 6 14.01 14.0209595240798 -0.0109595240797979 7 14.01 14.0691496333345 -0.0591496333345187 8 14.06 14.0593814743178 0.000618525682176241 9 14.09 14.1094836196208 -0.0194836196208268 10 14.13 14.1362660325633 -0.00626603256325708 11 14.12 14.1752312399698 -0.0552312399697783 12 14.13 14.1561101769251 -0.0261101769250942 13 14.14 14.161798259069 -0.0217982590689711 14 14.16 14.1681984250739 -0.008198425073914 15 14.21 14.1868445110208 0.0231554889792065 16 14.26 14.2406684822901 0.0193315177099489 17 14.29 14.2938609507532 -0.00386095075315751 18 14.32 14.3232233410371 -0.00322334103714716 19 14.33 14.3526910282223 -0.0226910282223276 20 14.39 14.3589437594832 0.0310562405168451 21 14.48 14.4240724860488 0.0559275139512287 22 14.44 14.5233085339985 -0.0833085339984585 23 14.46 14.469550697108 -0.00955069710803436 24 14.48 14.4879734645402 -0.00797346454018566 25 14.53 14.5066567011868 0.0233432988132236 26 14.58 14.5605116879708 0.0194883120292459 27 14.62 14.6137300499476 0.00626995005239017 28 14.62 14.6547654894877 -0.0347654894877341 29 14.61 14.6490242057221 -0.039024205722054 30 14.65 14.6325796239807 0.0174203760192633 31 14.68 14.6754564804124 0.0045435195876351 32 14.7 14.7062068117217 -0.00620681172171444 33 14.78 14.7251817990468 0.0548182009531999 34 14.84 14.8142346515115 0.0257653484884752 35 14.89 14.8784896233003 0.0115103766996807 36 14.89 14.9303904836034 -0.0403904836033533 37 15.13 14.9237202703771 0.206279729622947 38 15.25 15.1977859625426 0.0522140374574178 39 15.33 15.3264087551456 0.00359124485440887 40 15.36 15.4070018247697 -0.0470018247696657 41 15.4 15.4292397935863 -0.0292397935863367 42 15.4 15.4644110408576 -0.064411040857621 43 15.41 15.4537739962293 -0.0437739962293016 44 15.47 15.4565450189414 0.0134549810586186 45 15.54 15.5187670174076 0.0212329825923891 46 15.55 15.5922734998432 -0.0422734998432333 47 15.59 15.5952923193121 -0.00529231931206375 48 15.65 15.6344183288291 0.0155816711709384 49 15.75 15.6969915356803 0.0530084643197171 50 15.86 15.8057455224679 0.0542544775320497 51 15.89 15.9247052798498 -0.0347052798497653 52 15.94 15.9489739392956 -0.0089739392956485 53 15.93 15.9974919543516 -0.0674919543516275 54 15.95 15.976346117847 -0.0263461178470195 55 15.99 15.9919952359549 -0.00199523595487072 56 15.99 16.0316657363285 -0.0416657363285129 57 16.06 16.0247849238017 0.0352150761982983 58 16.08 16.10060045375 -0.0206004537500348 59 16.07 16.1171984291439 -0.0471984291439256 60 16.11 16.0994039300875 0.010596069912534 61 16.15 16.1411537988529 0.0088462011471222 62 16.18 16.1826146887118 -0.00261468871181947 63 16.3 16.2121828906824 0.0878171093176228 64 16.42 16.3466852880743 0.0733147119257467 65 16.49 16.4787927133275 0.0112072866725121 66 16.5 16.5506435203772 -0.0506435203771858 67 16.58 16.5522800879716 0.0277199120284166 68 16.64 16.6368578426145 0.00314215738552903 69 16.66 16.6973767485023 -0.037376748502254 70 16.81 16.7112042330983 0.0987957669017234 71 16.91 16.8775196810033 0.0324803189966758 72 16.92 16.982883584429 -0.0628835844290343 73 16.95 16.9824987888248 -0.0324987888248351 74 17.11 17.0071318352328 0.10286816476718 75 17.16 17.1841198119041 -0.0241198119040931 76 17.16 17.2301365892728 -0.0701365892727814 77 17.27 17.2185540093263 0.0514459906737486 78 17.34 17.3370499642349 0.00295003576511021 79 17.39 17.4075371425458 -0.0175371425457911 80 17.43 17.4546410029177 -0.0246410029177255 81 17.45 17.4905717091409 -0.0405717091408739 82 17.5 17.5038715677516 -0.00387156775156328 83 17.56 17.5532322047106 0.00676779528940585 84 17.65 17.6143498600003 0.0356501399997207 Extrapolation Forecasts of Exponential Smoothing t Forecast 95% Lower Bound 95% Upper Bound 85 17.7102372377718 17.6197425540635 17.8007319214801 86 17.7704744755435 17.6315258295282 17.9094231215588 87 17.8307117133153 17.6468666798591 18.0145567467714 88 17.890948951087 17.6626667155827 18.1192311865913 89 17.9511861888588 17.6778827762073 18.2244896015103 90 18.0114234266305 17.6920621793521 18.3307846739089 91 18.0716606644023 17.7049864047221 18.4383349240825 92 18.131897902174 17.7165466584179 18.5472491459302 93 18.1921351399458 17.7266910335562 18.6575792463355 94 18.2523723777176 17.7353990949092 18.769345660526 95 18.3126096154893 17.7426685540096 18.8825506769691 96 18.3728468532611 17.7485078328418 18.9971858736804

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/May/19/t1305828716jh9ppu2hwmljk4z/13k261305828787.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/19/t1305828716jh9ppu2hwmljk4z/13k261305828787.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/19/t1305828716jh9ppu2hwmljk4z/2zo431305828787.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/19/t1305828716jh9ppu2hwmljk4z/2zo431305828787.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/19/t1305828716jh9ppu2hwmljk4z/34am41305828787.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/19/t1305828716jh9ppu2hwmljk4z/34am41305828787.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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