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: Wed, 02 Dec 2009 14:14:08 -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/02/t1259788499auibuamhb4f9lre.htm/, Retrieved Wed, 02 Dec 2009 22:15:05 +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/02/t1259788499auibuamhb4f9lre.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:

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0.0314796223103059 -3.00870920563557 -2.07677512619799 -1.25010391965540 0.817975239137125 0.0252076485413113 0.554937772830776 0.230027371950115 2.35672227418686 1.41350455171120 2.73311719024401 1.31551925971717 -2.70076272244080 -0.721411049152714 -0.149388576811997 -0.118199629770334 -0.676562489695275 1.79699928690761 1.79845572032988 0.245100010770855 1.80710848932636 -1.75934771184948 -0.0186697168761931 0.189651523600062 -1.84149562719087 -1.07019530156943 -0.507291477584104 0.866365633831705 -1.76077926699189 -0.580719393339347 -0.435702079860853 -0.994868534845203 1.63136048315789 -1.1949403709466 -1.00525975426991 1.32302234837564 -0.628357549594746 0.632048410440518 -2.16903155809288 2.53779364144266 -0.632933703679292 -1.41749196342200 -0.455343045381255 0.812255211942954 0.627897309219833 0.650904313655623 -1.29800419154382 0.74391671726854 -1.50461634127457 -1.42734677658523 0.263353807408564 - etc...

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 0.0314796223103059 NA NA 0.202293392203127 NA 2 -3.00870920563557 NA NA 0.00723493478418439 NA 3 -2.07677512619799 NA NA 0.268676718707118 NA 4 -1.2501039196554 NA NA -0.527992125675796 NA 5 0.817975239137125 NA NA -0.659866406423514 NA 6 0.0252076485413113 NA NA 1.47635101637598 NA 7 0.554937772830776 0.302255032844237 0.148065125563697 2.04136545789244 1.83599183645936 8 0.230027371950115 0.0128857548456243 0.129525784385853 0.0994840904204682 17.8512919658896 9 2.35672227418686 2.95811137312947 0.305137647130388 9.6943507330167 0.796698290535834 10 1.4135045517112 -0.275834106792482 0.432608098766682 -0.637607357742155 -5.12447343132451 11 2.73311719024401 0.0729822211909641 0.417498372143877 0.17480839701529 37.4490820592125 12 1.31551925971717 -0.0596804478538848 0.429050618374456 -0.139098850573846 -22.0427176240022 13 -2.7007627224408 0.112209821878956 0.554688517785514 0.202293392203127 -24.0688620409199 14 -0.721411049152714 0.00439254445221462 0.607129792215508 0.00723493478418439 -164.235344001811 15 -0.149388576811997 0.157137525354634 0.584857244463851 0.268676718707118 -0.950686836100102 16 -0.118199629770334 -0.226906987985446 0.429754492446302 -0.527992125675796 0.520916657612658 17 -0.676562489695275 -0.120685954286853 0.182894527001265 -0.659866406423514 5.60597539036882 18 1.79699928690761 0.0314840469238202 0.0213255835330439 1.47635101637598 57.0765026254626 19 1.79845572032988 0.0208570871221622 0.0102172234969115 2.04136545789244 86.2275594763607 20 0.245100010770855 0.00313248957348443 0.0314873419482955 0.0994840904204682 78.2444777615709 21 1.80710848932636 0.0197962897668080 0.00204204389876120 9.6943507330167 91.2852110477945 22 -1.75934771184948 -0.0179505444380346 0.0281529756833417 -0.637607357742155 98.0108273552792 23 -0.0186697168761931 0.00419554649336973 0.024000829279401 0.17480839701529 -4.44988916359217 24 0.189651523600062 0.0167261473554643 -0.120246481451581 -0.139098850573846 11.3386256601467 25 -1.84149562719087 -0.0631980743687292 -0.312408001469818 0.202293392203127 29.1384768536880 26 -1.07019530156943 -0.00330754641435522 -0.457163265878435 0.00723493478418439 323.561688182101 27 -0.507291477584104 -0.138677879429349 -0.516151455536123 0.268676718707118 3.65805620674024 28 0.866365633831705 0.263973526332917 -0.499957316588856 -0.527992125675796 3.28201712447129 29 -1.76077926699189 0.341512711978781 -0.517548262275974 -0.659866406423514 -5.15582350299537 30 -0.580719393339347 -0.755053737841602 -0.511432396135063 1.47635101637598 0.769110017254391 31 -0.435702079860853 -0.84443366835317 -0.413661191869576 2.04136545789244 0.515969573679561 32 -0.994868534845203 -0.0290679530167517 -0.292186950635989 0.0994840904204682 34.2256138322457 33 1.63136048315789 -2.81620209533704 -0.290499299323441 9.6943507330167 -0.579276780547475 34 -1.1949403709466 0.184967111767499 -0.290095635694183 -0.637607357742155 -6.46028561255053 35 -1.00525975426991 -0.0303221311509812 -0.173459236905702 0.17480839701529 33.1526748322695 36 1.32302234837564 0.0224409838586924 -0.161331195521121 -0.139098850573846 58.9556303193532 37 -0.628357549594746 -0.0398548514453959 -0.197015092837915 0.202293392203127 15.7661495854687 38 0.632048410440518 -0.000886544625252148 -0.122536643618425 0.00723493478418439 -712.934681952138 39 -2.16903155809288 -0.0239258730926300 -0.0890507864163374 0.268676718707118 90.6563179406406 40 2.53779364144266 0.0284859971611649 -0.0539515568053305 -0.527992125675796 89.0891628993927 41 -0.632933703679292 -0.00710079171574598 0.0107609535000158 -0.659866406423514 89.1356526168432 42 -1.417491963422 -0.0377445859706507 -0.0255661326825262 1.47635101637598 37.5548420248724 43 -0.455343045381255 -0.175978597705121 -0.0862063169653147 2.04136545789244 2.58749104333842 44 0.812255211942954 -0.0207449431093243 -0.208525232744714 0.0994840904204682 -39.1543716298681 45 0.627897309219833 -1.87085434248978 -0.192983975308226 9.6943507330167 -0.335620627944884 46 0.650904313655623 0.137294253220976 -0.215327272425387 -0.637607357742155 4.74094361843382 47 -1.29800419154382 -0.0518887351224115 -0.296832051596886 0.17480839701529 25.0151442019482 48 0.74391671726854 0.0173344636633193 -0.124619747696021 -0.139098850573846 42.9154735743403 49 -1.50461634127457 -0.0213932838401383 -0.105753745128051 0.202293392203127 70.3312475316012 50 -1.42734677658523 -0.00221371176371059 -0.305975358416468 0.00723493478418439 644.775349701686 51 0.263353807408564 -0.119887448838984 -0.446214504240956 0.268676718707118 -2.19667538144268 52 -0.430830854870631 0.249744747011059 -0.473008469002074 -0.527992125675796 -1.72508475163865 53 0.379576092518008 0.240054585102167 -0.363792705258731 -0.659866406423514 1.58120742562139 54 1.70309353400146 -0.402295162472445 -0.272492894989138 1.47635101637598 -4.23344273775132 55 -3.12314448117342 NA NA 2.04136545789244 NA 56 -1.32526207118689 NA NA 0.0994840904204682 NA 57 -0.60032490743804 NA NA 9.6943507330167 NA 58 1.23607137604666 NA NA -0.637607357742155 NA 59 0.738007075905376 NA NA 0.17480839701529 NA 60 0.899100896289585 NA NA -0.139098850573846 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/02/t1259788499auibuamhb4f9lre/1joix1259788447.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t1259788499auibuamhb4f9lre/1joix1259788447.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t1259788499auibuamhb4f9lre/23ycf1259788447.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t1259788499auibuamhb4f9lre/23ycf1259788447.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t1259788499auibuamhb4f9lre/36vqx1259788447.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t1259788499auibuamhb4f9lre/36vqx1259788447.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t1259788499auibuamhb4f9lre/4mqln1259788447.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t1259788499auibuamhb4f9lre/4mqln1259788447.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')

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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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