Classical Decomposition

*The author of this computation has been verified*

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

Title produced by software: Classical Decomposition

Date of computation: Mon, 07 Dec 2009 15:22:41 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/07/t1260224694klr17nvid2qjshh.htm/, Retrieved Mon, 07 Dec 2009 23:24:59 +0100

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/Dec/07/t1260224694klr17nvid2qjshh.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:

Classical Decomposition

Dataseries X:

» Textbox « » Textfile « » CSV «

220206 220115 218444 214912 210705 209673 237041 242081 241878 242621 238545 240337 244752 244576 241572 240541 236089 236997 264579 270349 269645 267037 258113 262813 267413 267366 264777 258863 254844 254868 277267 285351 286602 283042 276687 277915 277128 277103 275037 270150 267140 264993 287259 291186 292300 288186 281477 282656 280190 280408 276836 275216 274352 271311 289802 290726 292300 278506 269826 265861 269034 264176 255198 253353 246057 235372 258556 260993 254663 250643 243422 247105 248541 245039 237080 237085 225554 226839 247934 248333 246969 245098 246263 255765 264319 268347 273046 273963 267430 271993 292710 295881 293299 288576

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 220206 NA NA 1.00980672858271 NA 2 220115 NA NA 1.00380005873708 NA 3 218444 NA NA 0.985244011995279 NA 4 214912 NA NA 0.97556863535601 NA 5 210705 NA NA 0.954822892747436 NA 6 209673 NA NA 0.945635036746163 NA 7 237041 236126.479057893 229069.25 1.03080827766229 1.00387301308077 8 242081 241065.788958836 231111.208333333 1.04307268651006 1.00421134432036 9 241878 242011.051667823 233094.083333333 1.03825480341231 0.9994502248269 10 242621 239314.61125234 235125.625 1.01781594946251 1.01381607554323 11 238545 235615.107306975 237251.166666667 0.993104104048557 1.01243507993402 12 240337 239942.226611225 239447.333333333 1.00206681473960 1.00164528517698 13 244752 244103.862372167 241733.25 1.00980672858271 1.00265517153860 14 244576 244985.936635283 244058.5 1.00380005873708 0.998326693193442 15 241572 242757.515210389 246393.291666667 0.985244011995279 0.99511646340027 16 240541 242494.738066241 248567.583333333 0.97556863535601 0.991943173357817 17 236089 239087.891049681 250400.25 0.954822892747436 0.98745695134741 18 236997 238443.844588538 252152.083333333 0.945635036746163 0.993932136973237 19 264579 261859.10444766 254032.791666667 1.03080827766229 1.01038686647186 20 270349 266950.02882684 255926.583333333 1.04307268651006 1.01273261212257 21 269645 267706.776536857 257843.041666667 1.03825480341231 1.00724009861916 22 267037 264197.878721814 259573.333333333 1.01781594946251 1.0107461925581 23 258113 259317.564337639 261118.208333333 0.993104104048557 0.995354867917581 24 262813 263187.128759955 262644.291666667 1.00206681473960 0.998578468629077 25 267413 266505.751441289 263917.583333333 1.00980672858271 1.00340423632062 26 267366 266078.619969515 265071.333333333 1.00380005873708 1.00483834451123 27 264777 262471.919475744 266402.958333333 0.985244011995279 1.00878219860189 28 258863 261234.232739329 267776.375 0.97556863535601 0.99092296321786 29 254844 257054.713853935 269217.166666667 0.954822892747436 0.991399831495829 30 254868 255908.068855925 270620.333333333 0.945635036746163 0.995935771542589 31 277267 280023.578413176 271654.375 1.03080827766229 0.990155906053351 32 285351 284200.669145877 272464.875 1.04307268651006 1.00404760079412 33 286602 283753.047784211 273298.083333333 1.03825480341231 1.01004025238860 34 283042 279080.934851827 274195.875 1.01781594946251 1.01419324881607 35 276687 273280.897695926 275178.5 0.993104104048557 1.01246374090832 36 277915 276683.382148708 276112.708333333 1.00206681473960 1.00445136184807 37 277128 279666.899137151 276950.916666667 1.00980672858271 0.990921703122594 38 277103 278665.310731022 277610.375 1.00380005873708 0.994393594498994 39 275037 273987.410436111 278090.916666667 0.985244011995279 1.00383079486104 40 270150 271737.489208424 278542.666666667 0.97556863535601 0.994158004429024 41 267140 266354.131849275 278956.583333333 0.954822892747436 1.00295046352489 42 264993 264166.654244968 279353.708333333 0.945635036746163 1.00312812287907 43 287259 288295.256486932 279678.833333333 1.03080827766229 0.99640557219165 44 291186 292002.070536457 279944.125 1.04307268651006 0.997205257706022 45 292300 290874.134656498 280156.791666667 1.03825480341231 1.00490200115313 46 288186 285439.188679122 280442.833333333 1.01781594946251 1.00962310512999 47 281477 279016.984242235 280954.416666667 0.993104104048557 1.00881672405874 48 282656 282100.012562999 281518.166666667 1.00206681473960 1.00197088767189 49 280190 284651.767977519 281887.375 1.00980672858271 0.984325521639229 50 280408 283045.685062338 281974.166666667 1.00380005873708 0.99068106245196 51 276836 277794.475402129 281955 0.985244011995279 0.996549695955108 52 275216 274672.97523221 281551.666666667 0.97556863535601 1.00197698651398 53 274352 267983.338194312 280662.875 0.954822892747436 1.02376514095466 54 271311 264283.834186605 279477.625 0.945635036746163 1.02658946520517 55 289802 286887.344181025 278313 1.03080827766229 1.01015958311893 56 290726 289110.368819918 277171.833333333 1.04307268651006 1.00558828514756 57 292300 286136.707770378 275593.916666667 1.03825480341231 1.02153967688259 58 278506 278659.050140775 273781.375 1.01781594946251 0.999450761994998 59 269826 269817.902305771 271691.458333333 0.993104104048557 1.00003001170108 60 265861 269571.045919958 269015.041666667 1.00206681473960 0.98623722400417 61 269034 268826.371454133 266215.666666667 1.00980672858271 1.00077235185203 62 264176 264676.855012491 263674.875 1.00380005873708 0.998107673553595 63 255198 257018.429662015 260867.791666667 0.985244011995279 0.992917124019437 64 253353 251831.946123927 258138.625 0.97556863535601 1.0060399560083 65 246057 244317.694738982 255877.5 0.954822892747436 1.00711903107499 66 235372 240187.359187539 253995.833333333 0.945635036746163 0.979951654392524 67 258556 260135.249404650 252360.458333333 1.03080827766229 0.99392912183849 68 260993 261507.92746906 250709.208333333 1.04307268651006 0.998030929792288 69 254663 258688.365532567 249156.916666667 1.03825480341231 0.98443932519238 70 250643 252137.607900641 247724.166666667 1.01781594946251 0.994072253191082 71 243422 244494.32696326 246192.041666667 0.993104104048557 0.995614102885008 72 247105 245488.541172457 244982.208333333 1.00206681473960 1.00658466101849 73 248541 246578.730362802 244184.083333333 1.00980672858271 1.00795798418749 74 245039 244138.227485679 243214 1.00380005873708 1.00368960045134 75 237080 238789.56810758 242365.916666667 0.985244011995279 0.992840691822811 76 237085 235906.43853083 241814.291666667 0.97556863535601 1.00499588513357 77 225554 230782.244764256 241701.625 0.954822892747436 0.977345550262774 78 226839 229014.681228383 242180.833333333 0.945635036746163 0.990499817667963 79 247934 250691.628219881 243199.083333333 1.03080827766229 0.988999918986276 80 248333 255373.052001989 244827.666666667 1.04307268651006 0.972432283098007 81 246969 256757.730725622 247297.416666667 1.03825480341231 0.961875614424702 82 245098 254792.495986818 250332.583333333 1.01781594946251 0.961951406970322 83 246263 251865.104244171 253614 0.993104104048557 0.977757521189836 84 255765 257771.917940319 257240.25 1.00206681473960 0.99221436548886 85 264319 NA 260987.333333333 NA NA 86 268347 NA 264834.166666667 NA NA 87 273046 NA 268745.75 NA NA 88 273963 NA 272487.75 NA NA 89 267430 NA NA NA NA 90 271993 NA NA NA NA 91 292710 NA NA NA NA 92 295881 NA NA NA NA 93 293299 NA NA NA NA 94 288576 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/07/t1260224694klr17nvid2qjshh/1vq8u1260224559.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/07/t1260224694klr17nvid2qjshh/1vq8u1260224559.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/07/t1260224694klr17nvid2qjshh/2ac3h1260224559.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/07/t1260224694klr17nvid2qjshh/2ac3h1260224559.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/07/t1260224694klr17nvid2qjshh/3aqdu1260224559.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/07/t1260224694klr17nvid2qjshh/3aqdu1260224559.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/07/t1260224694klr17nvid2qjshh/4stuo1260224559.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/07/t1260224694klr17nvid2qjshh/4stuo1260224559.ps (open in new window)

Parameters (Session):

par1 = multiplicative ; par2 = 12 ;

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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