Klassiek decompositie 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: Tue, 01 Dec 2009 12:46:49 -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/01/t12596969296dvfxko3t4cdtdx.htm/, Retrieved Tue, 01 Dec 2009 20:48:54 +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/01/t12596969296dvfxko3t4cdtdx.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 «

8.4 8.4 8.4 8.6 8.9 8.8 8.3 7.5 7.2 7.4 8.8 9.3 9.3 8.7 8.2 8.3 8.5 8.6 8.5 8.2 8.1 7.9 8.6 8.7 8.7 8.5 8.4 8.5 8.7 8.7 8.6 8.5 8.3 8 8.2 8.1 8.1 8 7.9 7.9 8 8 7.9 8 7.7 7.2 7.5 7.3 7 7 7 7.2 7.3 7.1 6.8 6.4 6.1 6.5 7.7 7.9 7.5 6.9 6.6 6.9 7.7 8 8 7.7 7.3 7.4 8.1 8.3 8.2

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 8.4 NA NA 1.03502746171878 NA 2 8.4 NA NA 0.99657350194391 NA 3 8.4 NA NA 0.971587648997756 NA 4 8.6 NA NA 0.990637074150072 NA 5 8.9 NA NA 1.02786447326418 NA 6 8.8 NA NA 1.03398937051682 NA 7 8.3 8.53776423862828 8.37083333333333 1.01994196977142 0.972151463546799 8 7.5 8.35395078582325 8.42083333333333 0.992057490646997 0.897778810563211 9 7.2 8.01874013719616 8.425 0.951779244771057 0.897896661671538 10 7.4 7.84846918960419 8.40416666666667 0.933878336888946 0.942859023999456 11 8.8 8.4211737820039 8.375 1.00551328740345 1.04498496620574 12 9.3 8.6936036683872 8.35 1.04115013992661 1.06975201018398 13 9.3 8.64247930535183 8.35 1.03502746171878 1.07608010056107 14 8.7 8.35876024755454 8.3875 0.99657350194391 1.04082420626256 15 8.2 8.21396391590186 8.45416666666667 0.971587648997756 0.998299978421523 16 8.3 8.43279809370249 8.5125 0.990637074150072 0.984252191001507 17 8.5 8.76254463457711 8.525 1.02786447326418 0.970037854809765 18 8.6 8.78029307130533 8.49166666666667 1.03398937051682 0.979466167035524 19 8.5 8.61001012815375 8.44166666666666 1.01994196977142 0.98722299666129 20 8.2 8.34155006719017 8.40833333333333 0.992057490646997 0.983030723780353 21 8.1 8.0028771497833 8.40833333333333 0.951779244771057 1.01213599164387 22 7.9 7.86792498828937 8.425 0.933878336888946 1.00407667990714 23 8.6 8.48820800116413 8.44166666666666 1.00551328740345 1.01317027090059 24 8.7 8.80205680796289 8.45416666666667 1.04115013992661 0.98840534545624 25 8.7 8.75891989479519 8.4625 1.03502746171878 0.993273155194603 26 8.5 8.45011281856606 8.47916666666667 0.99657350194391 1.00590372963120 27 8.4 8.25849501648092 8.5 0.971587648997756 1.01713447586233 28 8.5 8.43279809370249 8.5125 0.990637074150072 1.00796911126660 29 8.7 8.7368480227455 8.5 1.02786447326418 0.995782458084474 30 8.7 8.74582675895476 8.45833333333333 1.03398937051682 0.994760157019136 31 8.6 8.5760120624947 8.40833333333333 1.01994196977142 1.00279709698756 32 8.5 8.29608076553551 8.3625 0.992057490646997 1.02458018915530 33 8.3 7.91959646586584 8.32083333333333 0.951779244771057 1.04803319661219 34 8 7.72784323775603 8.275 0.933878336888946 1.03521768672976 35 8.2 8.26615715019587 8.22083333333333 1.00551328740345 0.991996625639484 36 8.1 8.49838801715096 8.1625 1.04115013992661 0.953121931318392 37 8.1 8.38803505434596 8.10416666666667 1.03502746171878 0.965661200450429 38 8 8.0265690802399 8.05416666666667 0.99657350194391 0.996689858397243 39 7.9 7.7807977557237 8.00833333333333 0.971587648997756 1.01532005432073 40 7.9 7.87556473949307 7.95 0.990637074150072 1.00310266772164 41 8 8.1072810328712 7.8875 1.02786447326418 0.986767322800798 42 8 8.09096682429411 7.825 1.03398937051682 0.988756989582385 43 7.9 7.90030050752114 7.74583333333333 1.01994196977142 0.999961962520178 44 8 7.59750694920492 7.65833333333333 0.992057490646997 1.05297699014770 45 7.7 7.21369352599397 7.57916666666667 0.951779244771057 1.06741435192023 46 7.2 7.01576100587821 7.5125 0.933878336888946 1.02626072837536 47 7.5 7.49526362985323 7.45416666666667 1.00551328740345 1.00063191508407 48 7.3 7.69149665870783 7.3875 1.04115013992661 0.949100067765796 49 7 7.56001308497093 7.30416666666667 1.03502746171878 0.92592432332105 50 7 7.16702443481328 7.19166666666667 0.99657350194391 0.976695428300485 51 7 6.85778948917582 7.05833333333333 0.971587648997756 1.02073707731167 52 7.2 6.89731062876987 6.9625 0.990637074150072 1.04388512965728 53 7.3 7.13509255190883 6.94166666666667 1.02786447326418 1.02311216664555 54 7.1 7.21207585935482 6.975 1.03398937051682 0.984459972199344 55 6.8 7.16084257943686 7.02083333333333 1.01994196977142 0.949608921654966 56 6.4 6.98160459042824 7.0375 0.992057490646997 0.916694710664993 57 6.1 6.67831770081025 7.01666666666667 0.951779244771057 0.91340368537123 58 6.5 6.52547487901151 6.9875 0.933878336888946 0.996096088103343 59 7.7 7.03021373442913 6.99166666666667 1.00551328740345 1.0952725323685 60 7.9 7.33577036089958 7.04583333333333 1.04115013992661 1.07691484484136 61 7.5 7.38319589359398 7.13333333333333 1.03502746171878 1.01582026375697 62 6.9 7.21270072031904 7.2375 0.99657350194391 0.95664582069264 63 6.6 7.13307265639186 7.34166666666667 0.971587648997756 0.925267457367874 64 6.9 7.35960793003991 7.42916666666667 0.990637074150072 0.93754994363709 65 7.7 7.69185247492693 7.48333333333333 1.02786447326418 1.00105924094353 66 8 7.77215343505143 7.51666666666667 1.03398937051682 1.02931575744774 67 8 7.71331114639638 7.5625 1.01994196977142 1.03716806546013 68 7.7 NA NA 0.992057490646997 NA 69 7.3 NA NA 0.951779244771057 NA 70 7.4 NA NA 0.933878336888946 NA 71 8.1 NA NA 1.00551328740345 NA 72 8.3 NA NA 1.04115013992661 NA 73 8.2 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/01/t12596969296dvfxko3t4cdtdx/1et901259696807.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/01/t12596969296dvfxko3t4cdtdx/1et901259696807.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/01/t12596969296dvfxko3t4cdtdx/22zqk1259696807.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/01/t12596969296dvfxko3t4cdtdx/22zqk1259696807.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/01/t12596969296dvfxko3t4cdtdx/3wbe21259696807.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/01/t12596969296dvfxko3t4cdtdx/3wbe21259696807.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/01/t12596969296dvfxko3t4cdtdx/4glh71259696807.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/01/t12596969296dvfxko3t4cdtdx/4glh71259696807.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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