ADHOCForecasting(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, 09 Dec 2009 10:49:53 -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/09/t1260381062ggpo6qvnlghgpa7.htm/, Retrieved Wed, 09 Dec 2009 18:51:07 +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/09/t1260381062ggpo6qvnlghgpa7.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:

ADHOCForecasting(1)

Dataseries X:

» Textbox « » Textfile « » CSV «

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

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 9.3 NA NA 1.0352168535 NA 2 9.3 NA NA 1.01178560674111 NA 3 8.7 NA NA 0.983000307083953 NA 4 8.2 NA NA 0.968708917284983 NA 5 8.3 NA NA 0.991189438397787 NA 6 8.5 NA NA 1.03209909341544 NA 7 8.6 8.79572008904865 8.49166666666667 1.03580609488306 0.977748258577221 8 8.5 8.51300257055876 8.44166666666666 1.00845045258347 0.998472622267997 9 8.2 8.38114673507462 8.40833333333333 0.996766707838408 0.97838640214751 10 8.1 8.12963216316457 8.40833333333333 0.966854172031466 0.996355042568982 11 7.9 8.06279428137143 8.425 0.957008223308181 0.97980919818982 12 8.6 8.5523718055022 8.44166666666666 1.01311413293215 1.00556900419918 13 8.7 8.75189581563124 8.45416666666667 1.0352168535 0.99407033439103 14 8.7 8.56223569704662 8.4625 1.01178560674111 1.01608975830937 15 8.5 8.33502343714935 8.47916666666667 0.983000307083953 1.01979317323996 16 8.4 8.23402579692235 8.5 0.968708917284983 1.02015711477849 17 8.5 8.43750009436116 8.5125 0.991189438397787 1.00740739614102 18 8.7 8.77284229403126 8.5 1.03209909341544 0.991696842187529 19 8.7 8.76119321921922 8.45833333333333 1.03580609488306 0.9930154240766 20 8.6 8.47938755547264 8.40833333333333 1.00845045258347 1.01422419293119 21 8.5 8.33546159429869 8.3625 0.996766707838408 1.01973956737007 22 8.3 8.04503242311182 8.32083333333333 0.966854172031466 1.03169254808168 23 8 7.9192430478752 8.275 0.957008223308181 1.01019755949358 24 8.2 8.32864243447969 8.22083333333333 1.01311413293215 0.984554213307666 25 8.1 8.44995756669374 8.1625 1.0352168535 0.958584695374906 26 8.1 8.1996791879644 8.10416666666667 1.01178560674111 0.987843525864926 27 8 7.91724830663867 8.05416666666667 0.983000307083953 1.01045207755982 28 7.9 7.75774391259057 8.00833333333333 0.968708917284983 1.01833730128402 29 7.9 7.8799560352624 7.95 0.991189438397787 1.00254366453923 30 8 8.1406815993143 7.8875 1.03209909341544 0.982718695283924 31 8 8.10518269245994 7.825 1.03580609488306 0.98702278573464 32 7.9 7.8112891306361 7.74583333333333 1.00845045258347 1.01135675147601 33 8 7.63357170419581 7.65833333333333 0.996766707838408 1.04800220787902 34 7.7 7.32794891218848 7.57916666666667 0.966854172031466 1.05077151768794 35 7.2 7.18952427760271 7.5125 0.957008223308181 1.00145708144139 36 7.5 7.55192159923171 7.45416666666667 1.01311413293215 0.993124716861866 37 7.3 7.64766450523124 7.3875 1.0352168535 0.954539780740456 38 7 7.3902507025715 7.30416666666667 1.01178560674111 0.947193847911585 39 7 7.06941054177876 7.19166666666667 0.983000307083953 0.990181565864855 40 7 6.83747044116983 7.05833333333333 0.968708917284983 1.02377042215079 41 7.2 6.9011564648446 6.9625 0.991189438397787 1.04330339946323 42 7.3 7.16448787345887 6.94166666666667 1.03209909341544 1.01891441913708 43 7.1 7.22474751180934 6.975 1.03580609488306 0.982733304990184 44 6.8 7.08016255251308 7.02083333333333 1.00845045258347 0.960429926511554 45 6.4 7.0147457064128 7.0375 0.996766707838408 0.912363793052283 46 6.1 6.78409344042078 7.01666666666667 0.966854172031466 0.899162143559987 47 6.5 6.68709496036592 6.9875 0.957008223308181 0.97202148893132 48 7.7 7.0833563127506 6.99166666666667 1.01311413293215 1.08705529695568 49 7.9 7.29396541361874 7.04583333333333 1.0352168535 1.08308712093009 50 7.5 7.21740399475323 7.13333333333333 1.01178560674111 1.03915479935060 51 6.9 7.11446472252011 7.2375 0.983000307083953 0.969855114771848 52 6.6 7.11193796773391 7.34166666666667 0.968708917284983 0.92801709322318 53 6.9 7.3637115360969 7.42916666666667 0.991189438397787 0.937027471293005 54 7.7 7.7235415490589 7.48333333333333 1.03209909341544 0.99695197482795 55 8 7.78580914653767 7.51666666666667 1.03580609488306 1.02751041663506 56 8 NA NA 1.00845045258347 NA 57 7.7 NA NA 0.996766707838408 NA 58 7.3 NA NA 0.966854172031466 NA 59 7.4 NA NA 0.957008223308181 NA 60 8.1 NA NA 1.01311413293215 NA 61 8.3 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/09/t1260381062ggpo6qvnlghgpa7/1h5g01260380991.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/09/t1260381062ggpo6qvnlghgpa7/1h5g01260380991.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/09/t1260381062ggpo6qvnlghgpa7/2zd9r1260380991.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/09/t1260381062ggpo6qvnlghgpa7/2zd9r1260380991.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/09/t1260381062ggpo6qvnlghgpa7/36zlr1260380991.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/09/t1260381062ggpo6qvnlghgpa7/36zlr1260380991.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/09/t1260381062ggpo6qvnlghgpa7/4n1t71260380991.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/09/t1260381062ggpo6qvnlghgpa7/4n1t71260380991.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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