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, 11 Dec 2009 08:40:47 -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/11/t1260546397e6owozl2sumasc0.htm/, Retrieved Fri, 11 Dec 2009 16:46:43 +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/11/t1260546397e6owozl2sumasc0.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 «

8.3 8.2 8 7.9 7.6 7.6 8.3 8.4 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

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 8.3 NA NA 1.03524932354549 NA 2 8.2 NA NA 1.03033694048115 NA 3 8 NA NA 1.00374983725856 NA 4 7.9 NA NA 0.96901694619278 NA 5 7.6 NA NA 0.941845105965402 NA 6 7.6 NA NA 0.936410597347787 NA 7 8.3 8.35498930226754 8.2 1.01890113442287 0.99341838747147 8 8.4 8.49886391056049 8.25 1.03016532249218 0.988367396913175 9 8.4 8.49054246786196 8.2875 1.02449984529254 0.98933607973758 10 8.4 8.3322035830397 8.28333333333333 1.00589982893839 1.00813667312430 11 8.4 8.18980512565796 8.25 0.992703651594904 1.02566543050988 12 8.6 8.31729656169873 8.225 1.01122146646793 1.03398982303975 13 8.9 8.52786630270601 8.2375 1.03524932354549 1.04363737470602 14 8.8 8.54750353540822 8.29583333333333 1.03033694048115 1.02954037556648 15 8.3 8.40222259605186 8.37083333333333 1.00374983725856 0.98783386242351 16 7.5 8.15993020106504 8.42083333333333 0.96901694619278 0.919125509066376 17 7.2 7.93504501775851 8.425 0.941845105965402 0.907367252975441 18 7.4 7.8697507285437 8.40416666666667 0.936410597347787 0.940309325574964 19 8.8 8.53329700079154 8.375 1.01890113442287 1.03125439079218 20 9.3 8.6018804428097 8.35 1.03016532249218 1.08115894679446 21 9.3 8.55457370819275 8.35 1.02449984529254 1.08713774844132 22 8.7 8.43698481522078 8.3875 1.00589982893839 1.03117407350369 23 8.2 8.39248212119192 8.45416666666667 0.992703651594904 0.977064935210778 24 8.3 8.60802273330826 8.5125 1.01122146646793 0.964216784405508 25 8.5 8.82550048322534 8.525 1.03524932354549 0.963118184193177 26 8.6 8.74927785291911 8.49166666666667 1.03033694048115 0.982938265828499 27 8.5 8.47332154285767 8.44166666666666 1.00374983725856 1.00314852410680 28 8.2 8.14781748923763 8.40833333333333 0.96901694619278 1.00640447712916 29 8.1 7.91934759932576 8.40833333333333 0.941845105965402 1.02281152562234 30 7.9 7.8892592826551 8.425 0.936410597347787 1.00136143546055 31 8.6 8.6012237430864 8.44166666666666 1.01890113442287 0.999857724537467 32 8.7 8.70918933056931 8.45416666666667 1.03016532249218 0.99894486958309 33 8.7 8.66982994078816 8.4625 1.02449984529254 1.00347989054202 34 8.5 8.52919229954013 8.47916666666667 1.00589982893839 0.99657736647095 35 8.4 8.43798103855669 8.5 0.992703651594904 0.995498800200767 36 8.5 8.60802273330826 8.5125 1.01122146646793 0.987450923788773 37 8.7 8.7996192501367 8.5 1.03524932354549 0.988679140846332 38 8.7 8.7149332882364 8.45833333333333 1.03033694048115 0.998286471308213 39 8.6 8.43986321494906 8.40833333333333 1.00374983725856 1.01897386023595 40 8.5 8.10340421253712 8.3625 0.96901694619278 1.04894187394099 41 8.3 7.83693615255378 8.32083333333333 0.941845105965402 1.05908735741012 42 8 7.74879769305294 8.275 0.936410597347787 1.03241823014327 43 8.2 8.37621640923468 8.22083333333333 1.01890113442287 0.978962290296081 44 8.1 8.40872444484243 8.1625 1.03016532249218 0.963285222762677 45 8.1 8.302717496225 8.10416666666667 1.02449984529254 0.975584199231497 46 8 8.10168487224131 8.05416666666667 1.00589982893839 0.98744892280497 47 7.9 7.94990174318919 8.00833333333333 0.992703651594904 0.993722973591222 48 7.9 8.03921065842005 7.95 1.01122146646793 0.98268354141532 49 8 8.16552903946509 7.8875 1.03524932354549 0.979728314152694 50 8 8.06238655926501 7.825 1.03033694048115 0.992262023309547 51 7.9 7.77487894776526 7.74583333333333 1.00374983725856 1.01609299039578 52 8 7.42105477959304 7.65833333333333 0.96901694619278 1.07801387236744 53 7.7 7.13840103229611 7.57916666666667 0.941845105965402 1.07867293602069 54 7.2 7.03478461257525 7.5125 0.936410597347787 1.02348549337664 55 7.5 7.59505887284381 7.45416666666667 1.01890113442287 0.987484116392607 56 7.3 7.61034631991098 7.3875 1.03016532249218 0.959220473436403 57 7 7.48311761999096 7.30416666666667 1.02449984529254 0.935438991537388 58 7 7.23409626978195 7.19166666666667 1.00589982893839 0.967639873585895 59 7 7.00683327417403 7.05833333333333 0.992703651594904 0.999024769977157 60 7.2 7.04062946028296 6.9625 1.01122146646793 1.02263583684045 61 7.3 7.18635572094498 6.94166666666667 1.03524932354549 1.01581389559159 62 7.1 7.18660015985603 6.975 1.03033694048115 0.987949773477064 63 6.8 7.0471603157528 7.02083333333333 1.00374983725856 0.964927672327772 64 6.4 6.81945675883169 7.0375 0.96901694619278 0.93849117698584 65 6.1 6.60861316019057 7.01666666666667 0.941845105965402 0.92303783746121 66 6.5 6.54316904896766 6.9875 0.936410597347787 0.993402424934371 67 7.7 NA NA 1.01890113442287 NA 68 7.9 NA NA 1.03016532249218 NA 69 7.5 NA NA 1.02449984529254 NA 70 6.9 NA NA 1.00589982893839 NA 71 6.6 NA NA 0.992703651594904 NA 72 6.9 NA NA 1.01122146646793 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/11/t1260546397e6owozl2sumasc0/1ha3o1260546045.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t1260546397e6owozl2sumasc0/1ha3o1260546045.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t1260546397e6owozl2sumasc0/2302j1260546045.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t1260546397e6owozl2sumasc0/2302j1260546045.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t1260546397e6owozl2sumasc0/3yro71260546045.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t1260546397e6owozl2sumasc0/3yro71260546045.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t1260546397e6owozl2sumasc0/4ecn51260546045.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t1260546397e6owozl2sumasc0/4ecn51260546045.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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