Raf Mattheussen exponential smoothing roze garnalen

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

Title produced by software: Exponential Smoothing

Date of computation: Wed, 13 Aug 2008 07:54:47 -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/Aug/13/t1218635811hpmgacyij5zn9cj.htm/, Retrieved Wed, 13 Aug 2008 13:56:51 +0000

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

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16,8 16,91 16,91 17,16 17,02 17,23 17,22 17,29 17,3 17,22 17,19 17,23 17,36 17,39 17,29 17,28 17,4 17,51 17,54 17,64 17,65 17,5 17,37 17,56 17,49 17,61 17,79 17,83 17,56 17,95 18,09 18,38 18,38 18,44 18,84 19,01 19,06 19,06 18,97 18,98 19,41 19,55 19,64 19,71 19,48 19,48 19,41 19,25 19,14 19,21 19,3 19,53 19,14 19,16 19,24 19,38 19,27 19,27 19,07 19,15 19,24 19,36 19,57 19,59 19,36 19,46 19,65 19,46 19,51 19,64 19,64 19,69 19,28 19,67 19,65 19,6 19,53 19,64 19,67 19,81 19,73 19,87 19,97 20,12 19,94 20,31 20,13 20,22 20,38 20,44 20,34 20,14 19,97 19,82 19,98 20,12

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 4 seconds R Server 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Estimated Parameters of Exponential Smoothing Parameter Value alpha 0.914696945069777 beta 0 gamma 0 Interpolation Forecasts of Exponential Smoothing t Observed Fitted Residuals 3 16.91 16.91 0 4 17.16 16.91 0.25 5 17.02 17.1386742362674 -0.118674236267445 6 17.23 17.0301232748951 0.199876725104875 7 17.22 17.2129499047391 0.00705009526089384 8 17.29 17.2193986053367 0.0706013946633028 9 17.3 17.2839774853529 0.0160225146471156 10 17.22 17.2986332305529 -0.0786332305529385 11 17.19 17.2267076547852 -0.0367076547851966 12 17.23 17.1931312750925 0.0368687249074995 13 17.36 17.226854985134 0.133145014865992 14 17.39 17.3486423234832 0.0413576765167996 15 17.29 17.3864720638483 -0.096472063848303 16 17.28 17.2982293617617 -0.0182293617616835 17 17.4 17.2815550202477 0.118444979752297 18 17.51 17.3898962813860 0.120103718614022 19 17.54 17.4997547858937 0.0402452141062533 20 17.64 17.5365669602904 0.103433039709586 21 17.65 17.6311768457321 0.0188231542679453 22 17.5 17.6483943274375 -0.148394327437519 23 17.37 17.5126584894647 -0.142658489464736 24 17.56 17.3821692049631 0.177830795036925 25 17.49 17.5448304899227 -0.0548304899226792 26 17.61 17.4946772082937 0.115322791706276 27 17.79 17.6001626135644 0.189837386435627 28 17.83 17.7738062909971 0.0561937090029261 29 17.56 17.8252065049542 -0.265206504954190 30 17.95 17.5826229250600 0.367377074940041 31 18.09 17.9186616131963 0.171338386803715 32 18.38 18.0753843121788 0.304615687821173 33 18.38 18.3540153512492 0.0259846487508177 34 18.44 18.3777834300803 0.0622165699197375 35 18.84 18.4346927365186 0.405307263481433 36 19.01 18.8054260522396 0.204573947760377 37 19.06 18.9925492172969 0.0674507827030943 38 19.06 19.054246242178 0.00575375782200993 39 18.97 19.0595091868805 -0.089509186880452 40 18.98 18.9776354070852 0.00236459291478042 41 19.41 18.9797982930007 0.430201706999298 42 19.55 19.3733024801568 0.176697519843238 43 19.64 19.5349271617588 0.105072838241220 44 19.71 19.6310369659078 0.0789630340921654 45 19.48 19.7032642119654 -0.223264211965379 46 19.48 19.4990451193372 -0.0190451193372354 47 19.41 19.4816246068610 -0.0716246068609756 48 19.25 19.4161097977734 -0.166109797773416 49 19.14 19.2641696732039 -0.124169673203912 50 19.21 19.1505920524540 0.0594079475460205 51 19.3 19.2049323205872 0.0950676794128107 52 19.53 19.2918904365210 0.238109563479039 53 19.14 19.5096885268271 -0.369688526827137 54 19.16 19.171535560711 -0.0115355607110104 55 19.24 19.160984018569 0.0790159814310165 56 19.38 19.2332596953956 0.146740304604375 57 19.27 19.3674826037359 -0.0974826037358554 58 19.27 19.2783155639012 -0.00831556390122046 59 19.07 19.2707093430042 -0.200709343004242 60 19.15 19.0871211201113 0.0628788798887001 61 19.24 19.1446362394549 0.0953637605450979 62 19.36 19.2318651798959 0.128134820104133 63 19.57 19.3490697084022 0.220930291597817 64 19.59 19.5511539712001 0.0388460287999202 65 19.36 19.5866863150715 -0.226686315071461 66 19.46 19.3793370351865 0.0806629648135306 67 19.65 19.4531192026817 0.196880797318322 68 19.46 19.6332054665316 -0.173205466531648 69 19.51 19.4747749554258 0.0352250445742364 70 19.64 19.5069951960878 0.133004803912236 71 19.64 19.6286542839059 0.0113457160941088 72 19.69 19.6390321757568 0.0509678242431981 73 19.28 19.6856522888889 -0.405652288888909 74 19.67 19.3146033794817 0.355396620518338 75 19.65 19.6396835825579 0.0103164174420876 76 19.6 19.6491199780763 -0.0491199780762521 77 19.53 19.604190084188 -0.074190084188011 78 19.64 19.5363286408268 0.103671359173230 79 19.67 19.6311565163538 0.0388434836462466 80 19.81 19.6666865321808 0.143313467819155 81 19.73 19.7977749233824 -0.0677749233823803 82 19.87 19.7357814080122 0.134218591987821 83 19.97 19.858550744075 0.111449255924992 84 20.12 19.9604930379999 0.159506962000105 85 19.94 20.1063935688588 -0.166393568858751 86 20.31 19.9541938797444 0.355806120255604 87 20.13 20.2796486509793 -0.149648650979326 88 20.22 20.1427654870947 0.077234512905278 89 20.38 20.2134116601031 0.166588339896869 90 20.44 20.3657895056910 0.0742104943089608 91 20.34 20.4336696181276 -0.0936696181275636 92 20.14 20.3479903045804 -0.207990304580427 93 19.97 20.1577422083766 -0.18774220837658 94 19.82 19.9860149839139 -0.166014983913868 95 19.98 19.8341615852920 0.145838414707956 96 20.12 19.9675595376992 0.152440462300770 Extrapolation Forecasts of Exponential Smoothing t Forecast 95% Lower Bound 95% Upper Bound 97 20.1069963628708 19.7946229267090 20.4193697990326 98 20.1069963628708 19.6836559584413 20.5303367673002 99 20.1069963628708 19.5962547134771 20.6177380122645 100 20.1069963628708 19.5217639587540 20.6922287669876 101 20.1069963628708 19.4557384253136 20.758254300428 102 20.1069963628708 19.3958164730860 20.8181762526555 103 20.1069963628708 19.3405651721973 20.8734275535442 104 20.1069963628708 19.2890374981104 20.9249552276311 105 20.1069963628708 19.2405688478166 20.973423877925 106 20.1069963628708 19.1946715450312 21.0193211807103 107 20.1069963628708 19.1509751770967 21.0630175486449 108 20.1069963628708 19.1091905490714 21.1048021766701

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/13/t1218635811hpmgacyij5zn9cj/1nz4y1218635680.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/13/t1218635811hpmgacyij5zn9cj/1nz4y1218635680.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/13/t1218635811hpmgacyij5zn9cj/24ggx1218635680.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/13/t1218635811hpmgacyij5zn9cj/24ggx1218635680.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/13/t1218635811hpmgacyij5zn9cj/3v6ng1218635680.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/13/t1218635811hpmgacyij5zn9cj/3v6ng1218635680.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=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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