Workshop 5: Time series analysis

*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, 07 Dec 2010 12:18:30 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/07/t1291724518bs741b5cdqco2dd.htm/, Retrieved Tue, 07 Dec 2010 13:22:15 +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/2010/Dec/07/t1291724518bs741b5cdqco2dd.htm/}, year = {2010}, } @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 = {2010}, 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 «

-5 -1 -2 -5 -4 -6 -2 -2 -2 -2 2 1 -8 -1 1 -1 2 2 1 -1 -2 -2 -1 -8 -4 -6 -3 -3 -7 -9 -11 -13 -11 -9 -17 -22 -25 -20 -24 -24 -22 -19 -18 -17 -11 -11 -12 -10 -15 -15 -15 -13 -8 -13 -9 -7 -4 -4 -2 0

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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 -5 NA NA -3.85850694444445 NA 2 -1 NA NA -1.23350694444444 NA 3 -2 NA NA -0.910590277777779 NA 4 -5 NA NA -0.868923611111111 NA 5 -4 NA NA 0.693576388888888 NA 6 -6 NA NA -0.254340277777777 NA 7 -2 -1.95225694444444 -2.45833333333333 0.50607638888889 -0.0477430555555562 8 -2 -2.57725694444444 -2.58333333333333 0.00607638888888966 0.577256944444443 9 -2 -0.421006944444444 -2.45833333333333 2.03732638888889 -1.57899305555556 10 -2 0.589409722222223 -2.16666666666667 2.75607638888889 -2.58940972222222 11 2 0.131076388888888 -1.75 1.88107638888889 1.86892361111111 12 1 -1.92100694444444 -1.16666666666667 -0.754340277777778 2.92100694444444 13 -8 -4.56684027777778 -0.708333333333333 -3.85850694444445 -3.43315972222222 14 -1 -1.77517361111111 -0.541666666666667 -1.23350694444444 0.77517361111111 15 1 -1.41059027777778 -0.5 -0.910590277777779 2.41059027777778 16 -1 -1.36892361111111 -0.5 -0.868923611111111 0.368923611111111 17 2 0.0685763888888883 -0.625 0.693576388888888 1.93142361111111 18 2 -1.37934027777778 -1.125 -0.254340277777777 3.37934027777778 19 1 -0.827256944444444 -1.33333333333333 0.50607638888889 1.82725694444444 20 -1 -1.36892361111111 -1.375 0.00607638888888966 0.36892361111111 21 -2 0.287326388888890 -1.75 2.03732638888889 -2.28732638888889 22 -2 0.756076388888889 -2 2.75607638888889 -2.75607638888889 23 -1 -0.577256944444445 -2.45833333333333 1.88107638888889 -0.422743055555555 24 -8 -4.04600694444444 -3.29166666666667 -0.754340277777778 -3.95399305555556 25 -4 -8.10850694444445 -4.25 -3.85850694444445 4.10850694444445 26 -6 -6.48350694444444 -5.25 -1.23350694444444 0.483506944444443 27 -3 -7.03559027777778 -6.125 -0.910590277777779 4.03559027777778 28 -3 -7.66059027777778 -6.79166666666667 -0.868923611111111 4.66059027777778 29 -7 -7.05642361111111 -7.75 0.693576388888888 0.0564236111111116 30 -9 -9.25434027777778 -9 -0.254340277777777 0.254340277777777 31 -11 -9.95225694444444 -10.4583333333333 0.50607638888889 -1.04774305555556 32 -13 -11.9105902777778 -11.9166666666667 0.00607638888888966 -1.08940972222222 33 -11 -11.3376736111111 -13.375 2.03732638888889 0.337673611111111 34 -9 -12.3689236111111 -15.125 2.75607638888889 3.36892361111111 35 -17 -14.7439236111111 -16.625 1.88107638888889 -2.25607638888889 36 -22 -18.4210069444444 -17.6666666666667 -0.754340277777778 -3.57899305555555 37 -25 -22.2335069444444 -18.375 -3.85850694444445 -2.76649305555556 38 -20 -20.0668402777778 -18.8333333333333 -1.23350694444444 0.0668402777777786 39 -24 -19.9105902777778 -19 -0.910590277777779 -4.08940972222222 40 -24 -19.9522569444444 -19.0833333333333 -0.868923611111111 -4.04774305555556 41 -22 -18.2647569444444 -18.9583333333333 0.693576388888888 -3.73524305555556 42 -19 -18.5043402777778 -18.25 -0.254340277777777 -0.495659722222221 43 -18 -16.8272569444444 -17.3333333333333 0.50607638888889 -1.17274305555555 44 -17 -16.7022569444444 -16.7083333333333 0.00607638888888966 -0.297743055555554 45 -11 -14.0876736111111 -16.125 2.03732638888889 3.08767361111111 46 -11 -12.5355902777778 -15.2916666666667 2.75607638888889 1.53559027777778 47 -12 -12.3689236111111 -14.25 1.88107638888889 0.368923611111111 48 -10 -14.1710069444444 -13.4166666666667 -0.754340277777778 4.17100694444444 49 -15 -16.6501736111111 -12.7916666666667 -3.85850694444445 1.65017361111111 50 -15 -13.2335069444444 -12 -1.23350694444444 -1.76649305555556 51 -15 -12.2022569444444 -11.2916666666667 -0.910590277777779 -2.79774305555556 52 -13 -11.5772569444444 -10.7083333333333 -0.868923611111111 -1.42274305555556 53 -8 -9.30642361111111 -10 0.693576388888888 1.30642361111111 54 -13 -9.42100694444444 -9.16666666666667 -0.254340277777777 -3.57899305555556 55 -9 NA NA 0.50607638888889 NA 56 -7 NA NA 0.00607638888888966 NA 57 -4 NA NA 2.03732638888889 NA 58 -4 NA NA 2.75607638888889 NA 59 -2 NA NA 1.88107638888889 NA 60 0 NA NA -0.754340277777778 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/07/t1291724518bs741b5cdqco2dd/1xmci1291724306.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/07/t1291724518bs741b5cdqco2dd/1xmci1291724306.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/07/t1291724518bs741b5cdqco2dd/2pdbk1291724306.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/07/t1291724518bs741b5cdqco2dd/2pdbk1291724306.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/07/t1291724518bs741b5cdqco2dd/3pdbk1291724306.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/07/t1291724518bs741b5cdqco2dd/3pdbk1291724306.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/07/t1291724518bs741b5cdqco2dd/404a51291724306.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/07/t1291724518bs741b5cdqco2dd/404a51291724306.ps (open in new window)

Parameters (Session):

par1 = additive ; par2 = 12 ;

Parameters (R input):

par1 = additive ; 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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