cijferreeks - Verkoop aantal eenmanswoningen - Anne-Sophie De Smedt

*Unverified author*

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

Title produced by software: Exponential Smoothing

Date of computation: Tue, 02 Jun 2009 07:52:13 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Jun/02/t12439510152utb6f71b5flsxk.htm/, Retrieved Tue, 02 Jun 2009 15:56:59 +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/2009/Jun/02/t12439510152utb6f71b5flsxk.htm/}, year = {2009}, } @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 = {2009}, 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 «

53 59 73 72 62 58 55 56 52 52 43 37 43 55 68 68 64 65 57 59 54 57 43 42 52 51 58 60 61 58 62 61 49 51 47 40 45 50 58 52 50 50 46 46 38 37 34 29 30 40 46 46 47 47 43 46 37 41 39 36 48 55 56 53 52 53 52 56 51 48 42 42 44 50 60 66 58 59 55 57 57 56 53 51 45 58 74 65 65 55 52 59 54 57 45 40 47 47 60 58 63 64 64 63 55 54 44

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 'George Udny Yule' @ 72.249.76.132 Estimated Parameters of Exponential Smoothing Parameter Value alpha 0.571865347172465 beta 0 gamma 0.440257094572627 Interpolation Forecasts of Exponential Smoothing t Observed Fitted Residuals 13 43 42.8811431623932 0.118856837606820 14 55 54.8394104718922 0.160589528107792 15 68 67.821543260933 0.178456739066945 16 68 67.7305603554387 0.269439644561288 17 64 63.774940754115 0.225059245885021 18 65 64.7939415406946 0.20605845930541 19 57 55.468743102527 1.53125689747303 20 59 58.0263797292745 0.973620270725512 21 54 55.0567899595376 -1.05678995953762 22 57 54.9260789385688 2.0739210614312 23 43 47.2940463958363 -4.29404639583629 24 42 38.5620605990372 3.43793940096275 25 52 46.2741327885578 5.72586721144224 26 51 61.4467211984748 -10.4467211984748 27 58 68.3662683347066 -10.3662683347066 28 60 62.2622718698977 -2.26227186989774 29 61 56.8504889533232 4.14951104667676 30 58 60.1101662861139 -2.11016628611392 31 62 49.7101849300527 12.2898150699473 32 61 58.3151596135205 2.68484038647953 33 49 55.9414466891155 -6.94144668911549 34 51 53.0356099907569 -2.03560999075687 35 47 41.853185200919 5.146814799081 36 40 39.9774975012094 0.0225024987906295 37 45 46.1676496343313 -1.16764963433133 38 50 54.3497144748269 -4.34971447482686 39 58 64.7710929832406 -6.7710929832406 40 52 62.250569488423 -10.2505694884230 41 50 53.4791085722009 -3.4791085722009 42 50 51.1963603847112 -1.19636038471117 43 46 44.0331956276124 1.96680437238761 44 46 44.9243630838981 1.07563691610189 45 38 39.8159501692132 -1.81595016921322 46 37 40.7659051327993 -3.76590513279933 47 34 29.9477948635093 4.05220513649071 48 29 26.4802596966426 2.5197403033574 49 30 33.8741646295526 -3.87416462955262 50 40 39.9086820999807 0.0913179000193196 51 46 52.4133291447466 -6.41332914474664 52 46 49.4415542369943 -3.44155423699426 53 47 45.8402812436958 1.15971875630424 54 47 46.6405913066959 0.359408693304104 55 43 40.9633397423783 2.03666025762174 56 46 41.7264798123636 4.2735201876364 57 37 37.9017922463409 -0.901792246340868 58 41 39.0069767659813 1.99302323401865 59 39 32.9558283418862 6.04417165811379 60 36 30.3385765496866 5.66142345031345 61 48 38.3239184238033 9.67608157619665 62 55 52.8548032438416 2.14519675615836 63 56 65.3079359462154 -9.3079359462154 64 53 61.2409835416346 -8.2409835416346 65 52 55.76237405281 -3.76237405281 66 53 53.5970599044756 -0.597059904475628 67 52 47.6889810974716 4.31101890252843 68 56 50.1743722669510 5.82562773304895 69 51 46.2617901083592 4.73820989164075 70 48 51.1379382053022 -3.13793820530225 71 42 42.916168941332 -0.916168941332018 72 42 36.2463956070077 5.75360439299234 73 44 45.0411727414269 -1.04117274142691 74 50 52.0237398541579 -2.02373985415791 75 60 59.9340089986085 0.0659910013915308 76 66 61.4287897137306 4.57121028626941 77 58 64.1211999459995 -6.12119994599954 78 59 61.2035829154875 -2.20358291548752 79 55 55.3019096977404 -0.301909697740356 80 57 55.4348144377615 1.56518556223854 81 57 48.8808663903735 8.11913360962652 82 56 54.2058778171306 1.79412218286937 83 53 49.2233627533916 3.7766372466084 84 51 46.4944236882140 4.50557631178595 85 45 53.2947539003563 -8.29475390035628 86 58 55.9440459000908 2.05595409990919 87 74 66.5812425884173 7.41875741158266 88 65 73.130001331359 -8.13000133135901 89 65 66.543623656313 -1.54362365631302 90 55 66.9821928435083 -11.9821928435083 91 52 55.8469165529275 -3.84691655292746 92 59 54.3044822298070 4.69551777019305 93 54 50.7760118971721 3.22398810282793 94 57 52.1094621469038 4.89053785309622 95 45 49.2713628183717 -4.27136281837166 96 40 42.0774486294968 -2.07744862949677 97 47 42.7004494819375 4.29955051806253 98 47 54.5029856456102 -7.5029856456102 99 60 60.6845870783719 -0.684587078371891 100 58 59.6685489748219 -1.66854897482192 101 63 58.0187138331827 4.98128616681734 102 64 60.221094051115 3.77890594888495 103 64 59.6324566757825 4.36754332421751 104 63 64.3977449246665 -1.39774492466651 105 55 57.1073811817044 -2.10738118170443 106 54 55.7061329110397 -1.70613291103971 107 44 47.3687057535314 -3.36870575353143 Extrapolation Forecasts of Exponential Smoothing t Forecast 95% Lower Bound 95% Upper Bound 108 41.1045192503759 31.8486925493829 50.360345951369 109 44.1175372069331 33.4551222299326 54.7799521839336 110 51.236657424738 39.3327144492062 63.1406004002699 111 62.9941516466638 49.9664648378837 76.0218384554439 112 62.1841388632226 48.1222261622203 76.246051564225 113 62.7419119532632 47.7167945150014 77.7670293915251 114 61.86903140038 45.9388424731729 77.799220327587 115 59.230320520282 42.4437876894840 76.01685335108 116 60.4112668377074 42.8100041073688 78.0125295680461 117 53.7864661269901 35.4065527723152 72.166379481665 118 53.6659871404481 34.5390955961248 72.7928786847714 119 45.9908610506625 26.1450870579140 65.836635043411

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t12439510152utb6f71b5flsxk/176d31243950731.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t12439510152utb6f71b5flsxk/176d31243950731.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t12439510152utb6f71b5flsxk/2s2h61243950731.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t12439510152utb6f71b5flsxk/2s2h61243950731.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t12439510152utb6f71b5flsxk/3upu91243950731.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t12439510152utb6f71b5flsxk/3upu91243950731.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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