ws 8 Ad hoc forecasting link 1

*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 13:02:57 -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/t1259784396ewiujhg40ml5kb2.htm/, Retrieved Wed, 02 Dec 2009 21:06:42 +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/t1259784396ewiujhg40ml5kb2.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:

ws 8 Ad hoc forecasting link 1

Dataseries X:

» Textbox « » Textfile « » CSV «

8.2 8.0 7.5 6.8 6.5 6.6 7.6 8.0 8.1 7.7 7.5 7.6 7.8 7.8 7.8 7.5 7.5 7.1 7.5 7.5 7.6 7.7 7.7 7.9 8.1 8.2 8.2 8.2 7.9 7.3 6.9 6.6 6.7 6.9 7.0 7.1 7.2 7.1 6.9 7.0 6.8 6.4 6.7 6.6 6.4 6.3 6.2 6.5 6.8 6.8 6.4 6.1 5.8 6.1 7.2 7.3 6.9 6.1 5.8 6.2 7.1 7.7 7.9 7.7 7.4 7.5 8.0 8.1

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 8.2 NA NA 1.04719037696786 NA 2 8 NA NA 1.04798979186281 NA 3 7.5 NA NA 1.02767691179036 NA 4 6.8 NA NA 1.01399383174475 NA 5 6.5 NA NA 0.990039435715469 NA 6 6.6 NA NA 0.957980648057883 NA 7 7.6 7.60186047960396 7.49166666666667 1.01470885156004 0.999755259964458 8 8 7.50513183142118 7.46666666666667 1.00515158456534 1.06593730525918 9 8.1 7.38266074585126 7.47083333333333 0.988197757392248 1.09716540944291 10 7.7 7.2421107924289 7.5125 0.964008092170236 1.06322593242426 11 7.5 7.22832576067654 7.58333333333333 0.953185814594709 1.03758467013225 12 7.6 7.56843382527575 7.64583333333333 0.9898769035783 1.00417076709039 13 7.8 8.02409626351622 7.6625 1.04719037696786 0.972072086854799 14 7.8 8.0040220353522 7.6375 1.04798979186281 0.974510060760568 15 7.8 7.8060625424743 7.59583333333333 1.02767691179036 0.99922335461171 16 7.5 7.68100327546647 7.575 1.01399383174475 0.97643494359069 17 7.5 7.50779905417564 7.58333333333333 0.990039435715469 0.998961206324335 18 7.1 7.28464451127349 7.60416666666667 0.957980648057883 0.974652913949646 19 7.5 7.74138294669347 7.62916666666667 1.01470885156004 0.9688191440269 20 7.5 7.69778588512953 7.65833333333333 1.00515158456534 0.974306133207522 21 7.6 7.60088775060871 7.69166666666667 0.988197757392248 0.999883204352197 22 7.7 7.4590126131672 7.7375 0.964008092170236 1.03230821548785 23 7.7 7.41896292359548 7.78333333333333 0.953185814594709 1.03788091129431 24 7.9 7.72928882210722 7.80833333333333 0.9898769035783 1.02208627233653 25 8.1 8.15935835387457 7.79166666666667 1.04719037696786 0.992725119880734 26 8.2 8.10008776627296 7.72916666666667 1.04798979186281 1.0123347100192 27 8.2 7.86601036232875 7.65416666666667 1.02767691179036 1.04245985223599 28 8.2 7.68945322406434 7.58333333333333 1.01399383174475 1.06639571905293 29 7.9 7.44592158944342 7.52083333333333 0.990039435715469 1.06098350689059 30 7.3 7.14493900009838 7.45833333333333 0.957980648057883 1.02170221465844 31 6.9 7.49616164089979 7.3875 1.01470885156004 0.920471079806087 32 6.6 7.34179469892931 7.30416666666667 1.00515158456534 0.898962756471862 33 6.7 7.11914134387999 7.20416666666667 0.988197757392248 0.941124733498892 34 6.9 6.84445745440867 7.1 0.964008092170236 1.00811496688544 35 7 6.67627230972377 7.00416666666667 0.953185814594709 1.04848928792265 36 7.1 6.85077307018148 6.92083333333333 0.9898769035783 1.03637938773703 37 7.2 7.19943384165403 6.875 1.04719037696786 1.00007863928726 38 7.1 7.19619657079129 6.86666666666667 1.04798979186281 0.986632303628039 39 6.9 7.04386883289645 6.85416666666667 1.02767691179036 0.979575310626945 40 7 6.91205795306003 6.81666666666667 1.01399383174475 1.01272299039406 41 6.8 6.69101651971038 6.75833333333333 0.990039435715469 1.01628803037156 42 6.4 6.41847034198782 6.7 0.957980648057883 0.99712231404 43 6.7 6.7562697699706 6.65833333333333 1.01470885156004 0.991671473773784 44 6.6 6.6633173793477 6.62916666666667 1.00515158456534 0.9904976191673 45 6.4 6.51798770813304 6.59583333333333 0.988197757392248 0.981898138901702 46 6.3 6.30220290256292 6.5375 0.964008092170236 0.999650455150846 47 6.2 6.1559917192575 6.45833333333333 0.953185814594709 1.00714885314170 48 6.5 6.33933666999936 6.40416666666667 0.9898769035783 1.02534387087548 49 6.8 6.7151082923064 6.4125 1.04719037696786 1.01264189704742 50 6.8 6.7726340299134 6.4625 1.04798979186281 1.00404066866240 51 6.4 6.69274588803475 6.5125 1.02767691179036 0.956259225595564 52 6.1 6.61630975213448 6.525 1.01399383174475 0.921964090032526 53 5.8 6.43525633215055 6.5 0.990039435715469 0.901284999483734 54 6.1 6.19893311014122 6.47083333333333 0.957980648057883 0.984040300422121 55 7.2 6.56601186030309 6.47083333333333 1.01470885156004 1.09655604546344 56 7.3 6.55442595768646 6.52083333333333 1.00515158456534 1.11375123422352 57 6.9 6.54269265206784 6.62083333333333 0.988197757392248 1.05461166631742 58 6.1 6.50705462214909 6.75 0.964008092170236 0.937444105546074 59 5.8 6.56109569046024 6.88333333333333 0.953185814594709 0.883998690711543 60 6.2 6.93738729924458 7.00833333333333 0.9898769035783 0.893708212121172 61 7.1 NA 7.1 NA NA 62 7.7 NA 7.16666666666667 NA NA 63 7.9 NA NA NA NA 64 7.7 NA NA NA NA 65 7.4 NA NA NA NA 66 7.5 NA NA NA NA 67 8 NA NA NA NA 68 8.1 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/02/t1259784396ewiujhg40ml5kb2/1atm31259784174.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t1259784396ewiujhg40ml5kb2/1atm31259784174.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t1259784396ewiujhg40ml5kb2/24fnw1259784174.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t1259784396ewiujhg40ml5kb2/24fnw1259784174.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t1259784396ewiujhg40ml5kb2/3wd791259784174.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t1259784396ewiujhg40ml5kb2/3wd791259784174.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t1259784396ewiujhg40ml5kb2/43a7z1259784174.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t1259784396ewiujhg40ml5kb2/43a7z1259784174.ps (open in new window)

Parameters (Session):

par1 = 0.01 ; par2 = 0.99 ; par3 = 0.005 ;

Parameters (R input):

par1 = multiplicative ; par2 = 12 ; par3 = 0.005 ;

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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