ws9 forcasting 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: Fri, 04 Dec 2009 13:34:46 -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/04/t1259958931qwdqzybuq834g7s.htm/, Retrieved Fri, 04 Dec 2009 21:35:36 +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/04/t1259958931qwdqzybuq834g7s.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 «

2.05 2.11 2.09 2.05 2.08 2.06 2.06 2.08 2.07 2.06 2.07 2.06 2.09 2.07 2.09 2.28 2.33 2.35 2.52 2.63 2.58 2.70 2.81 2.97 3.04 3.28 3.33 3.50 3.56 3.57 3.69 3.82 3.79 3.96 4.06 4.05 4.03 3.94 4.02 3.88 4.02 4.03 4.09 3.99 4.01 4.01 4.19 4.30 4.27 3.82 3.15 2.49 1.81 1.26 1.06 0.84 0.78 0.70 0.36 0.35

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 2.05 NA NA 0.183845486111111 NA 2 2.11 NA NA 0.127178819444444 NA 3 2.09 NA NA 0.0235329861111111 NA 4 2.05 NA NA -0.058862847222222 NA 5 2.08 NA NA -0.134383680555556 NA 6 2.06 NA NA -0.226258680555555 NA 7 2.06 2.04530381944444 2.07166666666667 -0.0263628472222219 0.0146961805555557 8 2.08 2.04436631944444 2.07166666666667 -0.0273003472222225 0.0356336805555557 9 2.07 1.99634548611111 2.07 -0.0736545138888888 0.0736545138888887 10 2.06 2.06030381944444 2.07958333333333 -0.0192795138888889 -0.000303819444444464 11 2.07 2.17853298611111 2.09958333333333 0.0789496527777777 -0.108532986111111 12 2.06 2.27467881944444 2.12208333333333 0.152595486111111 -0.214678819444444 13 2.09 2.33717881944444 2.15333333333333 0.183845486111111 -0.247178819444444 14 2.07 2.32259548611111 2.19541666666667 0.127178819444444 -0.252595486111111 15 2.09 2.26311631944444 2.23958333333333 0.0235329861111111 -0.173116319444444 16 2.28 2.22863715277778 2.2875 -0.058862847222222 0.0513628472222223 17 2.33 2.21061631944444 2.345 -0.134383680555556 0.119383680555555 18 2.35 2.18749131944444 2.41375 -0.226258680555555 0.162508680555555 19 2.52 2.46488715277778 2.49125 -0.0263628472222219 0.055112847222222 20 2.63 2.55394965277778 2.58125 -0.0273003472222225 0.076050347222222 21 2.58 2.60967881944444 2.68333333333333 -0.0736545138888888 -0.0296788194444448 22 2.7 2.76655381944444 2.78583333333333 -0.0192795138888889 -0.0665538194444446 23 2.81 2.96686631944444 2.88791666666667 0.0789496527777777 -0.156866319444445 24 2.97 3.14259548611111 2.99 0.152595486111111 -0.172595486111111 25 3.04 3.27342881944444 3.08958333333333 0.183845486111111 -0.233428819444445 26 3.28 3.31509548611111 3.18791666666667 0.127178819444444 -0.0350954861111115 27 3.33 3.31144965277778 3.28791666666667 0.0235329861111111 0.0185503472222219 28 3.5 3.33197048611111 3.39083333333333 -0.058862847222222 0.168029513888889 29 3.56 3.36103298611111 3.49541666666667 -0.134383680555556 0.198967013888889 30 3.57 3.36624131944444 3.5925 -0.226258680555555 0.203758680555555 31 3.69 3.65238715277778 3.67875 -0.0263628472222219 0.0376128472222228 32 3.82 3.72019965277778 3.7475 -0.0273003472222225 0.0998003472222222 33 3.79 3.73009548611111 3.80375 -0.0736545138888888 0.0599045138888892 34 3.96 3.82905381944444 3.84833333333333 -0.0192795138888889 0.130946180555556 35 4.06 3.96228298611111 3.88333333333333 0.0789496527777777 0.0977170138888885 36 4.05 4.07426215277778 3.92166666666667 0.152595486111111 -0.0242621527777778 37 4.03 4.14134548611111 3.9575 0.183845486111111 -0.111345486111111 38 3.94 4.10842881944444 3.98125 0.127178819444444 -0.168428819444444 39 4.02 4.02103298611111 3.9975 0.0235329861111111 -0.00103298611111136 40 3.88 3.94988715277778 4.00875 -0.058862847222222 -0.0698871527777771 41 4.02 3.88186631944444 4.01625 -0.134383680555556 0.138133680555556 42 4.03 3.80582465277778 4.03208333333333 -0.226258680555555 0.224175347222223 43 4.09 4.02613715277778 4.0525 -0.0263628472222219 0.0638628472222225 44 3.99 4.03019965277778 4.0575 -0.0273003472222225 -0.0401996527777770 45 4.01 3.94259548611111 4.01625 -0.0736545138888888 0.0674045138888895 46 4.01 3.90280381944444 3.92208333333333 -0.0192795138888889 0.107196180555556 47 4.19 3.85103298611111 3.77208333333333 0.0789496527777777 0.33896701388889 48 4.3 3.71717881944444 3.56458333333333 0.152595486111111 0.582821180555556 49 4.27 3.50676215277778 3.32291666666667 0.183845486111111 0.763237847222222 50 3.82 3.19259548611111 3.06541666666667 0.127178819444444 0.62740451388889 51 3.15 2.82311631944444 2.79958333333333 0.0235329861111111 0.326883680555556 52 2.49 2.46822048611111 2.52708333333333 -0.058862847222222 0.0217795138888892 53 1.81 2.09519965277778 2.22958333333333 -0.134383680555556 -0.285199652777778 54 1.26 1.67915798611111 1.90541666666667 -0.226258680555555 -0.419157986111111 55 1.06 NA NA -0.0263628472222219 NA 56 0.84 NA NA -0.0273003472222225 NA 57 0.78 NA NA -0.0736545138888888 NA 58 0.7 NA NA -0.0192795138888889 NA 59 0.36 NA NA 0.0789496527777777 NA 60 0.35 NA NA 0.152595486111111 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259958931qwdqzybuq834g7s/16vvb1259958884.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259958931qwdqzybuq834g7s/16vvb1259958884.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259958931qwdqzybuq834g7s/2mqdv1259958884.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259958931qwdqzybuq834g7s/2mqdv1259958884.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259958931qwdqzybuq834g7s/34h7f1259958884.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259958931qwdqzybuq834g7s/34h7f1259958884.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259958931qwdqzybuq834g7s/4kttl1259958884.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259958931qwdqzybuq834g7s/4kttl1259958884.ps (open in new window)

Parameters (Session):

par1 = 60 ; par2 = 1.7 ; par3 = 2 ; par4 = 0 ; par5 = 12 ; par6 = MA ; par7 = 0.95 ;

Parameters (R input):

par1 = additive ; par2 = 12 ; par3 = 2 ; par4 = 0 ; par5 = 12 ; par6 = MA ; par7 = 0.95 ;

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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