Maarten Verhaegen 2MAR03 - tweede zit - Oefening 9 - Additief decompositiemodel evolutie prijzen diesel

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

Title produced by software: Classical Decomposition

Date of computation: Sun, 17 Aug 2008 13:07:09 -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/17/t1219000129ijebpk7x9fhewho.htm/, Retrieved Sun, 17 Aug 2008 19:08:54 +0000

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

0.6200 0.6200 0.6100 0.6100 0.6100 0.6000 0.5900 0.5900 0.5900 0.5900 0.5900 0.5700 0.5800 0.5700 0.5900 0.6200 0.6200 0.6100 0.6400 0.6500 0.6700 0.6700 0.6900 0.7400 0.7300 0.7400 0.7500 0.7400 0.7600 0.7600 0.7800 0.7900 0.8900 0.8800 0.8800 0.8400 0.7600 0.7700 0.7600 0.7700 0.7800 0.7900 0.7800 0.7600 0.7800 0.7600 0.7400 0.7300 0.7200 0.7100 0.7300 0.7500 0.7500 0.7200 0.7200 0.7200 0.7400 0.7800 0.7400 0.7400 0.7500 0.7800 0.8100 0.7500 0.7000 0.7100 0.7100 0.7300 0.7400 0.7400 0.7500 0.7400 0.7400 0.7300 0.7600 0.8000 0.8300 0.8100 0.8300 0.8800 0.8900 0.9300 0.9100 0.9000 0.8600 0.8800 0.9300 0.9800 0.9700 1.0300 1.0600 1.0600 1.0800 1.0900 1.0400 1.0000

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 2 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 0.62 NA NA -0.0145717592592592 NA 2 0.62 NA NA -0.0149189814814815 NA 3 0.61 NA NA -0.00234953703703700 NA 4 0.61 NA NA -0.0017939814814814 NA 5 0.61 NA NA -0.00471064814814813 NA 6 0.6 NA NA -0.0158912037037038 NA 7 0.59 0.587372685185185 0.5975 -0.0101273148148148 0.00262731481481482 8 0.59 0.59119212962963 0.59375 -0.00255787037037041 -0.00119212962962967 9 0.59 0.613761574074074 0.590833333333333 0.0229282407407408 -0.0237615740740741 10 0.59 0.61681712962963 0.590416666666667 0.0264004629629629 -0.0268171296296297 11 0.59 0.604386574074074 0.59125 0.0131365740740740 -0.0143865740740741 12 0.57 0.596539351851852 0.592083333333333 0.00445601851851854 -0.0265393518518520 13 0.58 0.580011574074074 0.594583333333333 -0.0145717592592592 -1.15740740741499e-05 14 0.57 0.584247685185185 0.599166666666667 -0.0149189814814815 -0.0142476851851852 15 0.59 0.602650462962963 0.605 -0.00234953703703700 -0.012650462962963 16 0.62 0.609872685185185 0.611666666666667 -0.0017939814814814 0.0101273148148148 17 0.62 0.614456018518518 0.619166666666667 -0.00471064814814813 0.0055439814814815 18 0.61 0.614525462962963 0.630416666666667 -0.0158912037037038 -0.00452546296296297 19 0.64 0.633622685185185 0.64375 -0.0101273148148148 0.00637731481481485 20 0.65 0.654525462962963 0.657083333333333 -0.00255787037037041 -0.00452546296296308 21 0.67 0.693761574074074 0.670833333333333 0.0229282407407408 -0.0237615740740739 22 0.67 0.708900462962963 0.6825 0.0264004629629629 -0.0389004629629629 23 0.69 0.706469907407407 0.693333333333333 0.0131365740740740 -0.0164699074074074 24 0.74 0.709872685185185 0.705416666666667 0.00445601851851854 0.0301273148148149 25 0.73 0.702928240740741 0.7175 -0.0145717592592592 0.0270717592592594 26 0.74 0.714247685185185 0.729166666666667 -0.0149189814814815 0.0257523148148149 27 0.75 0.74181712962963 0.744166666666667 -0.00234953703703700 0.00818287037037047 28 0.74 0.760289351851852 0.762083333333333 -0.0017939814814814 -0.0202893518518519 29 0.76 0.774039351851852 0.77875 -0.00471064814814813 -0.0140393518518517 30 0.76 0.77494212962963 0.790833333333333 -0.0158912037037038 -0.0149421296296295 31 0.78 0.786122685185185 0.79625 -0.0101273148148148 -0.006122685185185 32 0.79 0.79619212962963 0.79875 -0.00255787037037041 -0.00619212962962967 33 0.89 0.823344907407407 0.800416666666667 0.0229282407407408 0.0666550925925926 34 0.88 0.828483796296296 0.802083333333333 0.0264004629629629 0.0515162037037037 35 0.88 0.81730324074074 0.804166666666667 0.0131365740740740 0.0626967592592593 36 0.84 0.810706018518519 0.80625 0.00445601851851854 0.0292939814814814 37 0.76 0.79292824074074 0.8075 -0.0145717592592592 -0.0329282407407405 38 0.77 0.791331018518518 0.80625 -0.0149189814814815 -0.0213310185185184 39 0.76 0.79806712962963 0.800416666666667 -0.00234953703703700 -0.0380671296296295 40 0.77 0.789039351851852 0.790833333333333 -0.0017939814814814 -0.0190393518518519 41 0.78 0.775289351851852 0.78 -0.00471064814814813 0.00471064814814826 42 0.79 0.75369212962963 0.769583333333333 -0.0158912037037038 0.0363078703703704 43 0.78 0.753206018518519 0.763333333333333 -0.0101273148148148 0.0267939814814815 44 0.76 0.756608796296296 0.759166666666667 -0.00255787037037041 0.00339120370370360 45 0.78 0.778344907407407 0.755416666666667 0.0229282407407408 0.00165509259259278 46 0.76 0.779733796296296 0.753333333333333 0.0264004629629629 -0.0197337962962962 47 0.74 0.764386574074074 0.75125 0.0131365740740740 -0.0243865740740741 48 0.73 0.751539351851852 0.747083333333333 0.00445601851851854 -0.0215393518518519 49 0.72 0.727094907407407 0.741666666666667 -0.0145717592592592 -0.00709490740740737 50 0.71 0.722581018518518 0.7375 -0.0149189814814815 -0.0125810185185184 51 0.73 0.73181712962963 0.734166666666667 -0.00234953703703700 -0.00181712962962965 52 0.75 0.731539351851852 0.733333333333333 -0.0017939814814814 0.0184606481481482 53 0.75 0.729456018518519 0.734166666666667 -0.00471064814814813 0.0205439814814814 54 0.72 0.71869212962963 0.734583333333333 -0.0158912037037038 0.0013078703703705 55 0.72 0.726122685185185 0.73625 -0.0101273148148148 -0.00612268518518511 56 0.72 0.737858796296296 0.740416666666667 -0.00255787037037041 -0.0178587962962962 57 0.74 0.769594907407407 0.746666666666667 0.0229282407407408 -0.0295949074074074 58 0.78 0.776400462962963 0.75 0.0264004629629629 0.00359953703703708 59 0.74 0.761053240740741 0.747916666666667 0.0131365740740740 -0.0210532407407408 60 0.74 0.749872685185185 0.745416666666667 0.00445601851851854 -0.00987268518518514 61 0.75 0.730011574074074 0.744583333333333 -0.0145717592592592 0.0199884259259259 62 0.78 0.729664351851852 0.744583333333333 -0.0149189814814815 0.0503356481481483 63 0.81 0.742650462962963 0.745 -0.00234953703703700 0.0673495370370371 64 0.75 0.741539351851852 0.743333333333333 -0.0017939814814814 0.00846064814814818 65 0.7 0.737372685185185 0.742083333333333 -0.00471064814814813 -0.0373726851851851 66 0.71 0.726608796296296 0.7425 -0.0158912037037038 -0.0166087962962963 67 0.71 0.731956018518518 0.742083333333333 -0.0101273148148148 -0.0219560185185184 68 0.73 0.737025462962963 0.739583333333333 -0.00255787037037041 -0.00702546296296291 69 0.74 0.758344907407407 0.735416666666667 0.0229282407407408 -0.0183449074074074 70 0.74 0.76181712962963 0.735416666666667 0.0264004629629629 -0.0218171296296296 71 0.75 0.756053240740741 0.742916666666667 0.0131365740740740 -0.00605324074074065 72 0.74 0.756956018518518 0.7525 0.00445601851851854 -0.0169560185185184 73 0.74 0.747094907407407 0.761666666666666 -0.0145717592592592 -0.00709490740740726 74 0.73 0.757997685185185 0.772916666666667 -0.0149189814814815 -0.0279976851851851 75 0.76 0.78306712962963 0.785416666666667 -0.00234953703703700 -0.0230671296296295 76 0.8 0.797789351851852 0.799583333333333 -0.0017939814814814 0.00221064814814820 77 0.83 0.809456018518518 0.814166666666667 -0.00471064814814813 0.0205439814814815 78 0.81 0.811608796296296 0.8275 -0.0158912037037038 -0.00160879629629618 79 0.83 0.829039351851852 0.839166666666667 -0.0101273148148148 0.000960648148148224 80 0.88 0.847858796296296 0.850416666666667 -0.00255787037037041 0.0321412037037038 81 0.89 0.88667824074074 0.86375 0.0229282407407408 0.00332175925925937 82 0.93 0.904733796296296 0.878333333333334 0.0264004629629629 0.0252662037037036 83 0.91 0.90480324074074 0.891666666666667 0.0131365740740740 0.00519675925925922 84 0.9 0.911122685185185 0.906666666666667 0.00445601851851854 -0.011122685185185 85 0.86 0.910844907407407 0.925416666666667 -0.0145717592592592 -0.0508449074074073 86 0.88 0.927581018518518 0.9425 -0.0149189814814815 -0.0475810185185184 87 0.93 0.95556712962963 0.957916666666667 -0.00234953703703700 -0.0255671296296296 88 0.98 0.970706018518518 0.9725 -0.0017939814814814 0.00929398148148153 89 0.97 0.979872685185185 0.984583333333333 -0.00471064814814813 -0.00987268518518525 90 1.03 0.978275462962963 0.994166666666667 -0.0158912037037038 0.0517245370370372 91 1.06 NA NA -0.0101273148148148 NA 92 1.06 NA NA -0.00255787037037041 NA 93 1.08 NA NA 0.0229282407407408 NA 94 1.09 NA NA 0.0264004629629629 NA 95 1.04 NA NA 0.0131365740740740 NA 96 1 NA NA 0.00445601851851854 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/17/t1219000129ijebpk7x9fhewho/1okto1219000025.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/17/t1219000129ijebpk7x9fhewho/1okto1219000025.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/17/t1219000129ijebpk7x9fhewho/2rvt51219000025.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/17/t1219000129ijebpk7x9fhewho/2rvt51219000025.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/17/t1219000129ijebpk7x9fhewho/3543s1219000025.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/17/t1219000129ijebpk7x9fhewho/3543s1219000025.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/17/t1219000129ijebpk7x9fhewho/4edgo1219000025.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/17/t1219000129ijebpk7x9fhewho/4edgo1219000025.ps (open in new window)

Parameters (Session):

par1 = 4 ;

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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