Classical Decomposition - Olieprijzen in constante dollar - Bram Op de Beeck

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

Title produced by software: Classical Decomposition

Date of computation: Sun, 25 May 2008 09:43:40 -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/25/t1211730298262hzcsr860132b.htm/, Retrieved Sun, 25 May 2008 17:45:03 +0200

User-defined keywords:

Dataseries X:

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23.11 18.64 14.94 16.90 15.46 11.15 13.13 12.48 12.95 12.59 10.58 10.58 12.39 15.53 13.06 10.22 16.33 19.72 21.31 18.84 24.84 15.67 15.57 12.73 13.56 15.54 17.22 12.14 11.07 12.02 11.55 6.92 10.33 8.38 12.11 11.46 12.75 13.32 13.00 11.90 11.79 12.55 11.84 11.25 11.15 10.99 11.70 14.01 17.51 17.27 16.90 15.79 15.45 16.24 16.71 16.77 16.64 17.80 16.87 16.13 15.76 15.66 15.54 15.30 15.05 14.69 14.39 14.18 13.70 13.66 13.27 13.56 13.14 14.19 22.57 23.09 23.31 22.91 22.36 43.06 64.67 64.68 56.90 48.79 45.21 41.40 22.17 25.52 20.28 22.87 27.63 22.95 21.35 18.38 17.15 18.27 19.40 20.52

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 6 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 23.11 NA NA -0.948049768518519 NA 2 18.64 NA NA -0.157841435185185 NA 3 14.94 NA NA 0.400630787037037 NA 4 16.9 NA NA -1.96193865740741 NA 5 15.46 NA NA -1.88534143518519 NA 6 11.15 NA NA -1.61735532407407 NA 7 13.13 11.8235474537037 13.9291666666667 -2.10561921296296 1.30645254629630 8 12.48 12.9830613425926 13.3529166666667 -0.369855324074075 -0.503061342592591 9 12.95 17.5838946759259 13.145 4.43889467592593 -4.63389467592593 10 12.59 15.3660474537037 12.7883333333333 2.57771412037037 -2.77604745370370 11 10.58 14.1969502314815 12.54625 1.65070023148148 -3.61695023148148 12 10.58 12.9176446759259 12.9395833333333 -0.0219386574074081 -2.33764467592592 13 12.39 12.6894502314815 13.6375 -0.948049768518519 -0.299450231481476 14 15.53 14.0854918981481 14.2433333333333 -0.157841435185185 1.44450810185186 15 13.06 15.4043807870370 15.00375 0.400630787037037 -2.34438078703703 16 10.22 13.6655613425926 15.6275 -1.96193865740741 -3.44556134259259 17 16.33 14.0784085648148 15.96375 -1.88534143518519 2.25159143518519 18 19.72 14.6438946759259 16.26125 -1.61735532407407 5.07610532407408 19 21.31 14.2939641203704 16.3995833333333 -2.10561921296296 7.01603587962963 20 18.84 16.0788946759259 16.44875 -0.369855324074075 2.76110532407408 21 24.84 21.0613946759259 16.6225 4.43889467592593 3.77860532407407 22 15.67 19.4535474537037 16.8758333333333 2.57771412037037 -3.7835474537037 23 15.57 18.3873668981481 16.7366666666667 1.65070023148148 -2.81736689814815 24 12.73 16.1747280092593 16.1966666666667 -0.0219386574074081 -3.44472800925926 25 13.56 14.5211168981481 15.4691666666667 -0.948049768518519 -0.961116898148148 26 15.54 14.4079918981481 14.5658333333333 -0.157841435185185 1.13200810185185 27 17.22 13.8652141203704 13.4645833333333 0.400630787037037 3.35478587962963 28 12.14 10.5943113425926 12.55625 -1.96193865740741 1.54568865740741 29 11.07 10.2229918981481 12.1083333333333 -1.88534143518519 0.847008101851854 30 12.02 10.2938946759259 11.91125 -1.61735532407407 1.72610532407407 31 11.55 9.71896412037037 11.8245833333333 -2.10561921296296 1.83103587962963 32 6.92 11.3284780092593 11.6983333333333 -0.369855324074075 -4.40847800925926 33 10.33 15.8688946759259 11.43 4.43889467592593 -5.53889467592592 34 8.38 13.8218807870370 11.2441666666667 2.57771412037037 -5.44188078703704 35 12.11 12.9148668981481 11.2641666666667 1.65070023148148 -0.804866898148148 36 11.46 11.2943113425926 11.31625 -0.0219386574074081 0.165688657407411 37 12.75 10.4023668981481 11.3504166666667 -0.948049768518519 2.34763310185185 38 13.32 11.3850752314815 11.5429166666667 -0.157841435185185 1.93492476851852 39 13 12.1581307870370 11.7575 0.400630787037037 0.841869212962962 40 11.9 9.93847800925926 11.9004166666667 -1.96193865740741 1.96152199074074 41 11.79 10.1067418981481 11.9920833333333 -1.88534143518519 1.68325810185185 42 12.55 10.4638946759259 12.08125 -1.61735532407407 2.08610532407407 43 11.84 10.2802141203704 12.3858333333333 -2.10561921296296 1.55978587962963 44 11.25 12.3788946759259 12.74875 -0.369855324074075 -1.12889467592593 45 11.15 17.5147280092593 13.0758333333333 4.43889467592593 -6.36472800925926 46 10.99 15.9781307870370 13.4004166666667 2.57771412037037 -4.98813078703704 47 11.7 15.3657002314815 13.715 1.65070023148148 -3.66570023148148 48 14.01 13.9993113425926 14.02125 -0.0219386574074081 0.0106886574074085 49 17.51 13.4298668981481 14.3779166666667 -0.948049768518519 4.08013310185185 50 17.27 14.6529918981481 14.8108333333333 -0.157841435185185 2.61700810185185 51 16.9 15.6702141203704 15.2695833333333 0.400630787037037 1.22978587962963 52 15.79 13.8201446759259 15.7820833333333 -1.96193865740741 1.96985532407408 53 15.45 14.3959085648148 16.28125 -1.88534143518519 1.05409143518519 54 16.24 14.9676446759259 16.585 -1.61735532407407 1.27235532407407 55 16.71 14.4947974537037 16.6004166666667 -2.10561921296296 2.21520254629630 56 16.77 16.0905613425926 16.4604166666667 -0.369855324074075 0.679438657407406 57 16.64 20.7755613425926 16.3366666666667 4.43889467592593 -4.1355613425926 58 17.8 18.8372974537037 16.2595833333333 2.57771412037037 -1.03729745370370 59 16.87 17.8732002314815 16.2225 1.65070023148148 -1.00320023148148 60 16.13 16.1193113425926 16.14125 -0.0219386574074081 0.0106886574074032 61 15.76 15.0319502314815 15.98 -0.948049768518519 0.728049768518517 62 15.66 15.6175752314815 15.7754166666667 -0.157841435185185 0.0424247685185204 63 15.54 15.9456307870370 15.545 0.400630787037037 -0.405630787037039 64 15.3 13.2880613425926 15.25 -1.96193865740741 2.01193865740741 65 15.05 13.0421585648148 14.9275 -1.88534143518519 2.00784143518519 66 14.69 13.0530613425926 14.6704166666667 -1.61735532407407 1.63693865740741 67 14.39 12.3485474537037 14.4541666666667 -2.10561921296296 2.0414525462963 68 14.18 13.9138946759259 14.28375 -0.369855324074075 0.266105324074076 69 13.7 18.9543113425926 14.5154166666667 4.43889467592593 -5.25431134259259 70 13.66 17.7106307870370 15.1329166666667 2.57771412037037 -4.05063078703704 71 13.27 17.4523668981481 15.8016666666667 1.65070023148148 -4.18236689814815 72 13.56 16.4663946759259 16.4883333333333 -0.0219386574074081 -2.90639467592592 73 13.14 16.2148668981481 17.1629166666667 -0.948049768518519 -3.07486689814815 74 14.19 18.5404918981481 18.6983333333333 -0.157841435185185 -4.35049189814815 75 22.57 22.4260474537037 22.0254166666667 0.400630787037037 0.143952546296301 76 23.09 24.3130613425926 26.275 -1.96193865740741 -1.22306134259259 77 23.31 28.3334085648148 30.21875 -1.88534143518519 -5.02340856481482 78 22.91 31.8872280092593 33.5045833333333 -1.61735532407407 -8.97722800925926 79 22.36 34.203130787037 36.30875 -2.10561921296296 -11.8431307870370 80 43.06 38.4088946759259 38.77875 -0.369855324074075 4.65110532407407 81 64.67 44.3347280092593 39.8958333333333 4.43889467592593 20.3352719907408 82 64.68 42.558130787037 39.9804166666667 2.57771412037037 22.1218692129630 83 56.9 41.6061168981481 39.9554166666667 1.65070023148148 15.2938831018519 84 48.79 39.8055613425926 39.8275 -0.0219386574074081 8.98443865740741 85 45.21 39.0973668981481 40.0454166666667 -0.948049768518519 6.11263310185186 86 41.4 39.2692418981482 39.4270833333333 -0.157841435185185 2.13075810185184 87 22.17 37.1847974537037 36.7841666666667 0.400630787037037 -15.0147974537037 88 25.52 31.0880613425926 33.05 -1.96193865740741 -5.56806134259259 89 20.28 27.5792418981482 29.4645833333333 -1.88534143518519 -7.29924189814815 90 22.87 24.9193113425926 26.5366666666667 -1.61735532407407 -2.04931134259259 91 27.63 22.0839641203704 24.1895833333333 -2.10561921296296 5.54603587962963 92 22.95 21.8743113425926 22.2441666666667 -0.369855324074075 1.07568865740740 93 21.35 NA NA 4.43889467592593 NA 94 18.38 NA NA 2.57771412037037 NA 95 17.15 NA NA 1.65070023148148 NA 96 18.27 NA NA -0.0219386574074081 NA 97 19.4 NA NA NA NA 98 20.52 NA NA NA NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211730298262hzcsr860132b/1m3mj1211730213.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211730298262hzcsr860132b/1m3mj1211730213.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211730298262hzcsr860132b/2yend1211730213.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211730298262hzcsr860132b/2yend1211730213.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211730298262hzcsr860132b/3go261211730213.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211730298262hzcsr860132b/3go261211730213.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211730298262hzcsr860132b/4dq861211730213.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211730298262hzcsr860132b/4dq861211730213.ps (open in new window)

Parameters (Session):

par1 = additive ; par2 = 12 ;

Parameters (R input):

par1 = additive ; par2 = 12 ;

R code (references can be found in the software module):

par2 <- as.numeric(par2) x <- ts(x,freq=par2) m <- decompose(x,type=par1) m$figure bitmap(file='test1.png') plot(m) dev.off() mylagmax <- length(x)/2 bitmap(file='test2.png') op <- par(mfrow = c(2,2)) acf(as.numeric(x),lag.max = mylagmax,main='Observed') acf(as.numeric(m$trend),na.action=na.pass,lag.max = mylagmax,main='Trend') acf(as.numeric(m$seasonal),na.action=na.pass,lag.max = mylagmax,main='Seasonal') acf(as.numeric(m$random),na.action=na.pass,lag.max = mylagmax,main='Random') par(op) dev.off() bitmap(file='test3.png') op <- par(mfrow = c(2,2)) spectrum(as.numeric(x),main='Observed') spectrum(as.numeric(m$trend[!is.na(m$trend)]),main='Trend') spectrum(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal') spectrum(as.numeric(m$random[!is.na(m$random)]),main='Random') par(op) dev.off() bitmap(file='test4.png') op <- par(mfrow = c(2,2)) cpgram(as.numeric(x),main='Observed') cpgram(as.numeric(m$trend[!is.na(m$trend)]),main='Trend') cpgram(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal') cpgram(as.numeric(m$random[!is.na(m$random)]),main='Random') par(op) dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Classical Decomposition by Moving Averages',6,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'t',header=TRUE) a<-table.element(a,'Observations',header=TRUE) a<-table.element(a,'Fit',header=TRUE) a<-table.element(a,'Trend',header=TRUE) a<-table.element(a,'Seasonal',header=TRUE) a<-table.element(a,'Random',header=TRUE) a<-table.row.end(a) for (i in 1:length(m$trend)) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,x[i]) if (par1 == 'additive') a<-table.element(a,m$trend[i]+m$seasonal[i]) else a<-table.element(a,m$trend[i]*m$seasonal[i]) a<-table.element(a,m$trend[i]) a<-table.element(a,m$seasonal[i]) a<-table.element(a,m$random[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.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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