klassieke decompositie van de tijdreeks met moving averages

*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: Fri, 04 Dec 2009 11:39:30 -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/04/t1259952035ec17wq4mgvvgp4y.htm/, Retrieved Fri, 04 Dec 2009 19:40:40 +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/04/t1259952035ec17wq4mgvvgp4y.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:

Dataseries X:

» Textbox « » Textfile « » CSV «

107.11 107.57 107.81 108.75 109.43 109.62 109.54 109.53 109.84 109.67 109.79 109.56 110.22 110.40 110.69 110.72 110.89 110.58 110.94 110.91 111.22 111.09 111.00 111.06 111.55 112.32 112.64 112.36 112.04 112.37 112.59 112.89 113.22 112.85 113.06 112.99 113.32 113.74 113.91 114.52 114.96 114.91 115.30 115.44 115.52 116.08 115.94 115.56 115.88 116.66 117.41 117.68 117.85 118.21 118.92 119.03 119.17 118.95 118.92 118.90

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 1 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 107.11 NA NA 0.997537193196223 NA 2 107.57 NA NA 1.00053837314138 NA 3 107.81 NA NA 1.00216513945625 NA 4 108.75 NA NA 1.00181946953812 NA 5 109.43 NA NA 1.00113455682792 NA 6 109.62 NA NA 1.00014340559154 NA 7 109.54 109.328699611791 109.147916666667 1.00165631145922 1.00193270741314 8 109.53 109.490673734648 109.395416666667 1.00087075922268 1.00035917456721 9 109.84 109.79176865484 109.633333333333 1.00144513823205 1.00043929837137 10 109.67 109.774294813808 109.835416666667 0.999443514171352 0.999049915884361 11 109.79 109.767261159592 109.978333333333 0.998080784029506 1.00020715503118 12 109.56 109.546972988661 110.079166666667 0.995165355133756 1.00011891712736 13 110.22 109.906154103377 110.1775 0.997537193196223 1.00285558073780 14 110.4 110.352712301674 110.293333333333 1.00053837314138 1.00042851414650 15 110.69 110.647382772132 110.408333333333 1.00216513945625 1.00038516254791 16 110.72 110.726096870701 110.525 1.00181946953812 0.9999449373646 17 110.89 110.760104555259 110.634583333333 1.00113455682792 1.00117276383282 18 110.58 110.763381810749 110.7475 1.00014340559154 0.99834438234233 19 110.94 111.049044326723 110.865416666667 1.00165631145922 0.999018052542607 20 110.91 111.097488332683 111.000833333333 1.00087075922268 0.99831239809741 21 111.22 111.322727909912 111.162083333333 1.00144513823205 0.999077206318592 22 111.09 111.249723301604 111.311666666667 0.999443514171352 0.99856428135852 23 111 111.214062429441 111.427916666667 0.998080784029506 0.99807522156133 24 111.06 111.011110017402 111.550416666667 0.995165355133756 1.00044040621331 25 111.55 111.418669872561 111.69375 0.997537193196223 1.00117870844796 26 112.32 111.905214343998 111.845 1.00053837314138 1.00370658023787 27 112.64 112.253352408111 112.010833333333 1.00216513945625 1.00344441910727 28 112.36 112.371585349418 112.1675 1.00181946953812 0.999896901433031 29 112.04 112.454107653291 112.326666666667 1.00113455682792 0.996317540889054 30 112.37 112.509048779925 112.492916666667 1.00014340559154 0.998764110252169 31 112.59 112.833661988306 112.647083333333 1.00165631145922 0.997840520426157 32 112.89 112.878204225134 112.78 1.00087075922268 1.00010450002237 33 113.22 113.055227999054 112.892083333333 1.00144513823205 1.00145744698288 34 112.85 112.972097624359 113.035 0.999443514171352 0.998919223180534 35 113.06 113.029321855395 113.246666666667 0.998080784029506 1.00027141757644 36 112.99 112.925559369340 113.474166666667 0.995165355133756 1.00057064699099 37 113.32 113.412912977959 113.692916666667 0.997537193196223 0.99918075485834 38 113.74 113.973410539479 113.912083333333 1.00053837314138 0.997952061464386 39 113.91 114.361239751434 114.114166666667 1.00216513945625 0.996054259708846 40 114.52 114.552629819558 114.344583333333 1.00181946953812 0.999715154339021 41 114.96 114.729185933683 114.599166666667 1.00113455682792 1.00201181647406 42 114.91 114.842716726306 114.82625 1.00014340559154 1.00058587323265 43 115.3 115.230542070268 115.04 1.00165631145922 1.00060277360918 44 115.44 115.368704297666 115.268333333333 1.00087075922268 1.00061798130410 45 115.52 115.702798583255 115.535833333333 1.00144513823205 0.99842010231824 46 116.08 115.748884854565 115.813333333333 0.999443514171352 1.00286063356767 47 115.94 115.842662065378 116.065416666667 0.998080784029506 1.00084025982213 48 115.56 115.760951327009 116.323333333333 0.995165355133756 0.998264083659426 49 115.88 116.324474660600 116.611666666667 0.997537193196223 0.996179009946986 50 116.66 116.975025658903 116.912083333333 1.00053837314138 0.997306898142328 51 117.41 117.467534114940 117.21375 1.00216513945625 0.99951021262706 52 117.68 117.699177803465 117.485416666667 1.00181946953812 0.99983706085443 53 117.85 117.862737096554 117.729166666667 1.00113455682792 0.99989193279515 54 118.21 118.009420784260 117.9925 1.00014340559154 1.00169968816394 55 118.92 NA NA 1.00165631145922 NA 56 119.03 NA NA 1.00087075922268 NA 57 119.17 NA NA 1.00144513823205 NA 58 118.95 NA NA 0.999443514171352 NA 59 118.92 NA NA 0.998080784029506 NA 60 118.9 NA NA 0.995165355133756 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259952035ec17wq4mgvvgp4y/1p34h1259951968.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259952035ec17wq4mgvvgp4y/1p34h1259951968.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259952035ec17wq4mgvvgp4y/2vaby1259951968.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259952035ec17wq4mgvvgp4y/2vaby1259951968.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259952035ec17wq4mgvvgp4y/3uslk1259951969.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259952035ec17wq4mgvvgp4y/3uslk1259951969.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259952035ec17wq4mgvvgp4y/4evdy1259951969.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259952035ec17wq4mgvvgp4y/4evdy1259951969.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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