voorspellen- uitvoer België naar EU -Tessa Buck

*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 12:08:18 -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/t124396617481cr5qtq520dxk3.htm/, Retrieved Tue, 02 Jun 2009 20:09:34 +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/t124396617481cr5qtq520dxk3.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 «

11025,1 10853,8 12252,6 11839,4 11669,1 11601,4 11178,4 9516,4 12102,8 12989 11610,2 10205,5 11356,2 11307,1 12648,6 11947,2 11714,1 12192,5 11268,8 9097,4 12639,8 13040,1 11687,3 11191,7 11391,9 11793,1 13933,2 12778,1 11810,3 13698,4 11956,6 10723,8 13938,9 13979,8 13807,4 12973,9 12509,8 12934,1 14908,3 13772,1 13012,6 14049,9 11816,5 11593,2 14466,2 13615,9 14733,9 13880,7 13527,5 13584 16170,2 13260,6 14741,9 15486,5 13154,5 12621,2 15031,6 15452,4 15428 13105,9 14716,8 14180 16202,2 14392,4 15140,6 15960,1 14351,3 13230,2 15202,1 17056 16077,7 13348,2 16707,5 16792,6 16831,3 17804,5 16370,2 17602,5 17065,6 14427,9 17818,5 18027,6

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 0.151057287538905 beta 0.0628426978359442 gamma 0.193352917851991 Interpolation Forecasts of Exponential Smoothing t Observed Fitted Residuals 13 11356.2 11204.6067190448 151.593280955216 14 11307.1 11209.7592691540 97.3407308459537 15 12648.6 12571.4749904354 77.1250095645864 16 11947.2 11880.3733503956 66.8266496044234 17 11714.1 11673.4804316841 40.6195683159076 18 12192.5 12131.8408719123 60.659128087711 19 11268.8 11362.1338650906 -93.3338650905916 20 9097.4 9650.01594479876 -552.615944798756 21 12639.8 12143.7016223049 496.098377695094 22 13040.1 13110.1082113040 -70.0082113040353 23 11687.3 11720.4306396411 -33.1306396411201 24 11191.7 10289.769607038 901.93039296201 25 11391.9 11625.4345969344 -233.534596934413 26 11793.1 11568.9564158721 224.143584127922 27 13933.2 12998.0924885712 935.10751142883 28 12778.1 12419.7299668009 358.370033199102 29 11810.3 12259.4361990709 -449.136199070866 30 13698.4 12680.2149505300 1018.18504947003 31 11956.6 12005.3393842018 -48.7393842018282 32 10723.8 10137.4049372083 586.395062791677 33 13938.9 13229.2659696772 709.634030322824 34 13979.8 14243.0957945779 -263.295794577907 35 13807.4 12748.1402832999 1059.25971670007 36 12973.9 11543.6928417424 1430.20715825758 37 12509.8 12934.1037373036 -424.303737303582 38 12934.1 12976.3311981753 -42.2311981753064 39 14908.3 14702.0251392523 206.274860747741 40 13772.1 13869.0434891948 -96.9434891948458 41 13012.6 13503.3941589387 -490.794158938741 42 14049.9 14270.2858591979 -220.385859197915 43 11816.5 13158.4419012346 -1341.9419012346 44 11593.2 11064.7514683299 528.448531670116 45 14466.2 14416.5592645315 49.6407354685143 46 13615.9 15227.0817932000 -1611.18179320002 47 14733.9 13662.7138081898 1071.18619181025 48 13880.7 12434.2383747763 1446.46162522367 49 13527.5 13560.5390884228 -33.0390884227636 50 13584 13728.5900635703 -144.590063570307 51 16170.2 15573.5724782049 596.627521795052 52 13260.6 14690.1934763267 -1429.59347632665 53 14741.9 14024.0564018549 717.843598145078 54 15486.5 15065.3355466502 421.164453349818 55 13154.5 13781.2421626330 -626.742162633022 56 12621.2 11990.6411027533 630.558897246705 57 15031.6 15531.8147219158 -500.214721915792 58 15452.4 16024.4896110769 -572.089611076859 59 15428 14996.1070578172 431.892942182811 60 13105.9 13634.0929504598 -528.192950459772 61 14716.8 14241.1005561881 475.699443811862 62 14180 14468.4051771226 -288.405177122626 63 16202.2 16510.1751009291 -307.975100929085 64 14392.4 15078.8821856548 -686.48218565476 65 15140.6 14861.7295627150 278.870437284973 66 15960.1 15813.5244853532 146.575514646833 67 14351.3 14236.0319698116 115.268030188397 68 13230.2 12681.9366631210 548.263336879014 69 15202.1 16167.6232390865 -965.523239086522 70 17056 16585.9101180026 470.089881997425 71 16077.7 15834.0886753871 243.611324612908 72 13348.2 14201.6257030140 -853.425703013965 73 16707.5 14952.4436004646 1755.05639953544 74 16792.6 15250.5196626887 1542.08033731127 75 16831.3 17745.6814828752 -914.38148287516 76 17804.5 16067.9941618602 1736.50583813976 77 16370.2 16414.9668672790 -44.7668672789805 78 17602.5 17416.608096819 185.891903180993 79 17065.6 15709.8888745940 1355.71112540598 80 14427.9 14277.5964180937 150.303581906297 81 17818.5 17847.9542355833 -29.4542355832891 82 18027.6 18794.2166467020 -766.61664670198 Extrapolation Forecasts of Exponential Smoothing t Forecast 95% Lower Bound 95% Upper Bound 83 17755.9265203261 17314.6459925816 18197.2070480707 84 15721.4804959129 15237.9106120639 16205.0503797620 85 17234.2087977579 16680.357545681 17788.0600498349 86 17252.2944631895 16645.0554009737 17859.5335254054 87 19299.4560377763 18594.5227471723 20004.3893283803 88 18091.132127324 17363.0771195854 18819.1871350626 89 17865.780530542 17090.4032417399 18641.1578193441 90 19006.7585424187 18138.8893153478 19874.6277694896 91 17325.2603581154 16464.3139437842 18186.2067724465 92 15343.6887505734 14507.5237477592 16179.8537533876 93 19096.2113941679 18038.6635171310 20153.7592712048 94 19967.5263935678 18896.6645279828 21038.3882591529

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t124396617481cr5qtq520dxk3/1xdrr1243966093.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t124396617481cr5qtq520dxk3/1xdrr1243966093.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t124396617481cr5qtq520dxk3/231xf1243966093.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t124396617481cr5qtq520dxk3/231xf1243966093.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t124396617481cr5qtq520dxk3/3sk1y1243966093.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t124396617481cr5qtq520dxk3/3sk1y1243966093.ps (open in new window)

Parameters (Session):

par1 = 12 ; par2 = Triple ; par3 = multiplicative ;

Parameters (R input):

par1 = 12 ; par2 = Triple ; 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')

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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