exponential smoothing prijs eenpersoonskamers - sam roggeman

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

Title produced by software: Exponential Smoothing

Date of computation: Wed, 28 May 2008 13:30:07 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/May/28/t1212003091a1lhslv8h8vn2a9.htm/, Retrieved Wed, 28 May 2008 21:31:31 +0200

User-defined keywords:

Dataseries X:

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430,00 433,87 434,55 434,55 434,55 434,55 434,71 434,71 434,71 434,71 434,73 436,34 437,55 439,58 439,65 439,76 439,76 439,76 440,06 440,13 441,18 441,14 441,14 441,19 449,06 456,46 456,79 456,87 457,25 455,93 456,00 456,22 456,22 456,58 457,61 457,61 460,43 460,43 462,18 462,37 462,59 463,19 463,48 464,30 461,41 463,35 463,35 463,35 464,27 472,28 472,36 472,56 472,56 472,56 474,15 474,59 474,97 474,99 474,99 474,99 478,34 485,70 485,75 485,85 485,84 485,85 485,84 486,00 488,79 489,71 489,71 489,71

Text written by user:

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 3 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Estimated Parameters of Exponential Smoothing Parameter Value alpha 1 beta 0.211289943060778 gamma 0 Interpolation Forecasts of Exponential Smoothing t Observed Fitted Residuals 3 434.55 437.74 -3.19 4 434.55 437.745985081636 -3.19598508163614 5 434.55 437.070705575714 -2.52070557571415 6 434.55 436.538105838149 -1.9881058381485 7 434.71 436.118039068807 -1.40803906880734 8 434.71 435.980534574132 -1.27053457413166 9 434.71 435.712083396307 -1.00208339630660 10 434.71 435.500353252559 -0.790353252558873 11 434.73 435.333359558828 -0.603359558827776 12 436.34 435.225875751998 1.11412424800204 13 437.55 437.071279000921 0.478720999079144 14 439.58 438.382427933558 1.19757206644164 15 439.65 440.665462867288 -1.01546286728797 16 439.76 440.520905775878 -0.760905775878314 17 439.76 440.470134037818 -0.71013403781842 18 439.76 440.320089857402 -0.560089857402204 19 440.06 440.201748503323 -0.141748503322788 20 440.13 440.471798470127 -0.341798470126776 21 441.18 440.469579890835 0.710420109164602 22 441.14 441.66968451525 -0.52968451525004 23 441.14 441.517767504183 -0.377767504182657 24 441.19 441.437949029734 -0.247949029733718 25 449.06 441.435559893359 7.62444010664069 26 456.46 450.916527409362 5.54347259063826 27 456.79 459.487807417397 -2.69780741739663 28 456.87 459.247787841786 -2.37778784178602 29 457.25 458.825385184084 -1.57538518408444 30 455.93 458.87252213824 -2.94252213824041 31 456 456.930796803197 -0.930796803196529 32 456.22 456.804128799648 -0.584128799647942 33 456.22 456.90070825883 -0.68070825883018 34 456.58 456.756881449581 -0.176881449581003 35 457.61 457.07950817817 0.530491821829571 36 457.61 458.221595764999 -0.611595764999038 37 460.43 458.092371730636 2.33762826936379 38 460.43 461.406289074567 -0.976289074567319 39 462.18 461.200009011591 0.979990988408872 40 462.37 463.157071251732 -0.78707125173213 41 462.59 463.180771011769 -0.590771011768879 42 463.19 463.27594703833 -0.0859470383302323 43 463.48 463.857787293495 -0.377787293495203 44 464.3 464.067964637764 0.232035362236445 45 461.41 464.936991376239 -3.52699137623853 46 463.35 461.301773569177 2.04822643082275 47 463.35 463.674543215121 -0.324543215121366 48 463.35 463.605970497678 -0.255970497677595 49 464.27 463.551886505798 0.718113494201873 50 472.28 464.623616665099 7.6563833349008 51 472.36 474.251333463982 -1.89133346398182 52 472.56 473.931713724068 -1.37171372406823 53 472.56 473.841884409414 -1.28188440941415 54 472.56 473.571035125539 -1.01103512553851 55 474.15 473.357413571431 0.792586428568939 56 474.59 475.114879112794 -0.52487911279411 57 474.97 475.443977434938 -0.473977434937979 58 474.99 475.723830769698 -0.733830769697931 59 474.99 475.588779708152 -0.598779708152165 60 474.99 475.462263577711 -0.472263577710748 61 478.34 475.362479033267 2.97752096673338 62 485.7 479.34159926879 6.35840073121005 63 485.75 488.045065397245 -2.29506539724491 64 485.85 487.61014116014 -1.76014116014028 65 485.84 487.338241034635 -1.49824103463538 66 485.85 487.011677771736 -1.16167777173587 67 485.84 486.776226941491 -0.93622694149093 68 486 486.568411604331 -0.568411604331232 69 488.79 486.608311948817 2.18168805118296 70 489.71 489.859280692928 -0.149280692927903 71 489.71 490.747739183819 -1.03773918381904 72 489.71 490.528475330758 -0.81847533075802 Extrapolation Forecasts of Exponential Smoothing t Forecast 95% Lower Bound 95% Upper Bound 73 490.355539724725 486.144219835997 494.566859613454 74 491.001079449451 484.386191948613 497.615966950289 75 491.646619174176 482.72204151673 500.571196831623 76 492.292158898902 481.023051286338 503.561266511465 77 492.937698623627 479.249223224916 506.626174022339 78 493.583238348353 477.385203730852 509.781272965853 79 494.228778073078 475.424996309759 513.032559836398 80 494.874317797804 473.366748661039 516.381886934569 81 495.519857522529 471.210603889119 519.829111155939 82 496.165397247255 468.957703909563 523.373090584947 83 496.81093697198 466.609689550074 527.012184393887 84 497.456476696706 464.168436179268 530.744517214143

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/28/t1212003091a1lhslv8h8vn2a9/1y2y81212003001.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/28/t1212003091a1lhslv8h8vn2a9/1y2y81212003001.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/28/t1212003091a1lhslv8h8vn2a9/27pyc1212003001.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/28/t1212003091a1lhslv8h8vn2a9/27pyc1212003001.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/28/t1212003091a1lhslv8h8vn2a9/3acgk1212003001.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/28/t1212003091a1lhslv8h8vn2a9/3acgk1212003001.ps (open in new window)

Parameters (Session):

par1 = 12 ; par2 = Double ; par3 = multiplicative ;

Parameters (R input):

par1 = 12 ; par2 = Double ; 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=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')

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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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