Classical Decomposition omzet product A

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Sun, 15 Aug 2010 13:23:32 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Aug/15/t1281878579hdz3zrcxjxulmfo.htm/, Retrieved Sun, 15 Aug 2010 15:23:05 +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/2010/Aug/15/t1281878579hdz3zrcxjxulmfo.htm/}, year = {2010}, } @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 = {2010}, 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:

Sebastien Delforge

Dataseries X:

» Textbox « » Textfile « » CSV «

75 74 73 71 91 90 75 65 66 66 67 69 75 79 75 77 100 100 94 83 83 83 84 88 89 98 94 84 111 98 98 83 79 78 80 94 98 104 94 90 115 104 114 99 96 98 104 111 117 125 117 118 151 145 155 133 124 125 131 133 136 141 130 137 177 183 191 166 156 153 164 164 168 173 164 165 205 207 215 190 169 175 188 188 196 201 194 197 237 236 244 222 195 199 207 204 212 222 214 217 258 256 251 223 198 206 214 212 227 238 228 235 275 278 278 251 225 232 238 239

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 75 NA NA 0.982705774643998 NA 2 74 NA NA 1.02106199604345 NA 3 73 NA NA 0.954079594210181 NA 4 71 NA NA 0.943324478234067 NA 5 91 NA NA 1.16817539239470 NA 6 90 NA NA 1.12759139926081 NA 7 75 82.824338111512 73.5 1.12686174301377 0.905530931005116 8 65 71.9172941501188 73.7083333333333 0.975700994687876 0.903815984293294 9 66 67.0531287247342 74 0.906123361145057 0.984294115058262 10 66 67.345024089628 74.3333333333333 0.905986871160915 0.980027862372757 11 67 70.0458676725882 74.9583333333333 0.934464048995062 0.956516097611563 12 69 72.2597692254168 75.75 0.953924346210122 0.954888186602869 13 75 75.6273985736443 76.9583333333333 0.982705774643998 0.991704083632688 14 79 80.1533666894104 78.5 1.02106199604345 0.985610502252767 15 75 76.286614220389 79.9583333333333 0.954079594210181 0.983134469480162 16 77 76.7630294162972 81.375 0.943324478234067 1.00308704053898 17 100 96.7151876953443 82.7916666666667 1.16817539239470 1.03396376911352 18 100 95.0465583626924 84.2916666666666 1.12759139926081 1.05211594951608 19 94 96.5344893181796 85.6666666666667 1.12686174301377 0.973745245496396 20 83 84.9266407459572 87.0416666666667 0.975700994687876 0.977314059180553 21 83 80.3051828814806 88.625 0.906123361145057 1.03355720044242 22 83 81.2745722337271 89.7083333333333 0.905986871160915 1.02122961362763 23 84 84.5300604320117 90.4583333333333 0.934464048995062 0.993729326238469 24 88 86.6481281140861 90.8333333333333 0.953924346210122 1.01560185909768 25 89 89.3443333447168 90.9166666666667 0.982705774643998 0.996145996821217 26 98 93.0017301396238 91.0833333333333 1.02106199604345 1.05374383737671 27 94 86.7417364402756 90.9166666666667 0.954079594210181 1.08367671501160 28 84 85.4101704667761 90.5416666666667 0.943324478234067 0.98348943153878 29 111 105.330481214255 90.1666666666667 1.16817539239470 1.05382600288526 30 98 101.765123783288 90.25 1.12759139926081 0.963001825740358 31 98 102.403560896376 90.875 1.12686174301377 0.956997970990166 32 83 89.2766410139406 91.5 0.975700994687876 0.929694476151264 33 79 83.136818385059 91.75 0.906123361145057 0.95024083835036 34 78 83.3507921468042 92 0.905986871160915 0.935803943682023 35 80 86.3600525279603 92.4166666666667 0.934464048995062 0.926354230436565 36 94 88.5559768065064 92.8333333333333 0.953924346210122 1.06147550272512 37 98 92.1286663728748 93.75 0.982705774643998 1.06372971473789 38 104 97.0859781237976 95.0833333333333 1.02106199604345 1.07121545263093 39 94 92.028927524857 96.4583333333333 0.954079594210181 1.02141796637379 40 90 92.4457988669386 98 0.943324478234067 0.973543428723474 41 115 116.622843340737 99.8333333333333 1.16817539239470 0.98608468723408 42 104 114.497509999941 101.541666666667 1.12759139926081 0.90831669614521 43 114 116.113712103044 103.041666666667 1.12686174301377 0.981796188712251 44 99 102.164024985443 104.708333333333 0.975700994687876 0.96902994977054 45 96 96.5398931019963 106.541666666667 0.906123361145057 0.994407564741906 46 98 98.4505733328195 108.666666666667 0.905986871160915 0.995423354912355 47 104 104.036997454784 111.333333333333 0.934464048995062 0.999644381751793 48 111 109.264084488818 114.541666666667 0.953924346210122 1.01588733863743 49 117 115.918335334048 117.958333333333 0.982705774643998 1.00933126466003 50 125 123.633590020927 121.083333333333 1.02106199604345 1.01105209335781 51 117 117.987843150659 123.666666666667 0.954079594210181 0.99162758531489 52 118 118.819579070899 125.958333333333 0.943324478234067 0.993102323057294 53 151 149.769820099937 128.208333333333 1.16817539239470 1.00821380368383 54 145 146.868779753721 130.25 1.12759139926081 0.987275854290788 55 155 148.698797505192 131.958333333333 1.12686174301377 1.04237561164264 56 133 130.174774374607 133.416666666667 0.975700994687876 1.02170332646218 57 124 121.986857494153 134.625 0.906123361145057 1.01650294586811 58 125 123.176465024919 135.958333333333 0.905986871160915 1.01480424831734 59 131 128.800294753153 137.833333333333 0.934464048995062 1.01707841780225 60 133 134.026370642522 140.5 0.953924346210122 0.992342024650807 61 136 141.100170809301 143.583333333333 0.982705774643998 0.963854254888226 62 141 149.543038170530 146.458333333333 1.02106199604345 0.942872377911785 63 130 142.316872803019 149.166666666667 0.954079594210181 0.91345458510695 64 137 143.070879198833 151.666666666667 0.943324478234067 0.957567331431601 65 177 180.142380302199 154.208333333333 1.16817539239470 0.982556129785077 66 183 176.890900759040 156.875 1.12759139926081 1.03453597225604 67 191 179.734448010696 159.5 1.12686174301377 1.06267886937641 68 166 158.226177971884 162.166666666667 0.975700994687876 1.04913107380687 69 156 149.434844308839 164.916666666667 0.906123361145057 1.04393323204856 70 153 151.752800919453 167.5 0.905986871160915 1.00821862313572 71 164 158.703144320995 169.833333333333 0.934464048995062 1.03337587104318 72 164 164.074987548141 172 0.953924346210122 0.999542967826714 73 168 170.990804788056 174 0.982705774643998 0.982508972972185 74 173 179.706911303646 176 1.02106199604345 0.962678612330531 75 164 169.388881288733 177.541666666667 0.954079594210181 0.968186334027752 76 165 168.855081603898 179 0.943324478234067 0.977169288793208 77 205 211.342398074074 180.916666666667 1.16817539239470 0.969989939870698 78 207 206.25526011479 182.916666666667 1.12759139926081 1.00361076796197 79 215 208.563327602798 185.083333333333 1.12686174301377 1.03086195675522 80 190 182.862628087753 187.416666666667 0.975700994687876 1.03903133180839 81 169 172.01241805737 189.833333333333 0.906123361145057 0.982487205915766 82 175 174.326973792546 192.416666666667 0.905986871160915 1.00386071181534 83 188 182.29836155812 195.083333333333 0.934464048995062 1.03127641078695 84 188 188.519298919775 197.625 0.953924346210122 0.997245380591 85 196 196.582101002743 200.041666666667 0.982705774643998 0.997038891131116 86 201 206.850142698468 202.583333333333 1.02106199604345 0.971717966339546 87 194 195.586316813087 205 0.954079594210181 0.991889428468541 88 197 195.346777367638 207.083333333333 0.943324478234067 1.00846301461759 89 237 244.002635086442 208.875 1.16817539239470 0.971300985811233 90 236 237.170057644524 210.333333333333 1.12759139926081 0.99506658784779 91 244 238.519068937915 211.666666666667 1.12686174301377 1.02297900577296 92 222 208.027582909078 213.208333333333 0.975700994687876 1.06716617525201 93 195 194.741012366092 214.916666666667 0.906123361145057 1.00132990801866 94 199 196.221656512268 216.583333333333 0.905986871160915 1.01415920921837 95 207 203.985714695214 218.291666666667 0.934464048995062 1.01477694312707 96 204 209.863356166227 220 0.953924346210122 0.972061076915292 97 212 217.300814418154 221.125 0.982705774643998 0.975606099625777 98 222 226.122687873788 221.458333333333 1.02106199604345 0.9817679158489 99 214 211.447890066831 221.625 0.954079594210181 1.01206968739372 100 217 209.457339354556 222.041666666667 0.943324478234067 1.03601048628177 101 258 260.065046731869 222.625 1.16817539239470 0.992059499122162 102 256 251.734779884976 223.25 1.12759139926081 1.01694330881483 103 251 252.651793298212 224.208333333333 1.12686174301377 0.993462174652912 104 223 220.020574302116 225.5 0.975700994687876 1.01354157767897 105 198 205.463472139642 226.75 0.906123361145057 0.963674943960019 106 206 206.640505530619 228.083333333333 0.905986871160915 0.996900387322543 107 214 214.498435246408 229.541666666667 0.934464048995062 0.997676275606227 108 212 220.515511365573 231.166666666667 0.953924346210122 0.961383617357166 109 227 229.175175861769 233.208333333333 0.982705774643998 0.99050867593495 110 238 240.460100068231 235.5 1.02106199604345 0.9897691963551 111 228 226.872176839896 237.791666666667 0.954079594210181 1.00497118322667 112 235 226.397874776176 240 0.943324478234067 1.03799560942137 113 275 282.795792908883 242.083333333333 1.16817539239470 0.972433136898205 114 278 275.367216294484 244.208333333333 1.12759139926081 1.00956099183100 115 278 NA NA 1.12686174301377 NA 116 251 NA NA 0.975700994687876 NA 117 225 NA NA 0.906123361145057 NA 118 232 NA NA 0.905986871160915 NA 119 238 NA NA 0.934464048995062 NA 120 239 NA NA 0.953924346210122 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Aug/15/t1281878579hdz3zrcxjxulmfo/1cwvt1281878610.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/15/t1281878579hdz3zrcxjxulmfo/1cwvt1281878610.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/15/t1281878579hdz3zrcxjxulmfo/2cwvt1281878610.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/15/t1281878579hdz3zrcxjxulmfo/2cwvt1281878610.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/15/t1281878579hdz3zrcxjxulmfo/3n5cw1281878610.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/15/t1281878579hdz3zrcxjxulmfo/3n5cw1281878610.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/15/t1281878579hdz3zrcxjxulmfo/4n5cw1281878610.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/15/t1281878579hdz3zrcxjxulmfo/4n5cw1281878610.ps (open in new window)

Parameters (Session):

Parameters (R input):

par1 = multiplicative ; 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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