Ad hoc forecasting 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: Thu, 03 Dec 2009 10:14:39 -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/03/t12598607234gicc79acg1yz84.htm/, Retrieved Thu, 03 Dec 2009 18:18:48 +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/03/t12598607234gicc79acg1yz84.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:

Uitleg in Word document

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

96.96 93.11 95.62 98.30 96.38 100.82 99.06 94.03 102.07 99.31 98.64 101.82 99.14 97.63 100.06 101.32 101.49 105.43 105.09 99.48 108.53 104.34 106.10 107.35 103.00 104.50 105.17 104.84 106.18 108.86 107.77 102.74 112.63 106.26 108.86 111.38 106.85 107.86 107.94 111.38 111.29 113.72 111.88 109.87 113.72 111.71 114.81 112.05 111.54 110.87 110.87 115.48 111.63 116.24 113.56 106.01 110.45 107.77 108.61 108.19

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 96.96 NA NA 0.982441725464283 NA 2 93.11 NA NA 0.98046891946843 NA 3 95.62 NA NA 0.986208918331083 NA 4 98.3 NA NA 1.00490527685339 NA 5 96.38 NA NA 0.99781901202507 NA 6 100.82 NA NA 1.02784167491245 NA 7 99.06 98.9798924245661 98.1008333333333 1.00896077088607 1.00080933180944 8 94.03 94.7631636974127 98.38 0.963236061165 0.992263199445791 9 102.07 102.077113669109 98.7533333333333 1.03365739893109 0.999930310832145 10 99.31 98.4866160139208 99.0641666666667 0.9941699337694 1.00836036427491 11 98.64 100.037131673927 99.4029166666667 1.00638024545484 0.986033869118907 12 101.82 101.196251049337 99.8079166666667 1.01391006273888 1.00616375551658 13 99.14 98.4910110299512 100.25125 0.982441725464283 1.00658932184026 14 97.63 98.7622257293386 100.729583333333 0.98046891946843 0.988535842312409 15 100.06 99.8298195988292 101.225833333333 0.986208918331083 1.00230572790871 16 101.32 102.203472471842 101.704583333333 1.00490527685339 0.991355748973346 17 101.49 102.002048504263 102.225 0.99781901202507 0.994980017443067 18 105.43 105.627434524471 102.76625 1.02784167491245 0.998130840483251 19 105.09 104.08187072268 103.1575 1.00896077088607 1.00968592580360 20 99.48 99.795670768641 103.604583333333 0.963236061165 0.996836829030662 21 108.53 107.607611443973 104.10375 1.03365739893109 1.00857177799646 22 104.34 103.854305181331 104.463333333333 0.9941699337694 1.00467669412280 23 106.1 105.474100949997 104.805416666667 1.00638024545484 1.00593414918322 24 107.35 106.606306159102 105.14375 1.01391006273888 1.00697607737940 25 103 103.547720461060 105.398333333333 0.982441725464283 0.994710453705587 26 104.5 103.582456054675 105.645833333333 0.98046891946843 1.00885810184729 27 105.17 104.491300419474 105.9525 0.986208918331083 1.00649527355676 28 104.84 106.724290086086 106.203333333333 1.00490527685339 0.98234431838744 29 106.18 106.166279847781 106.398333333333 0.99781901202507 1.00012923267387 30 108.86 109.651434681753 106.68125 1.02784167491245 0.99278226788322 31 107.77 107.968471692197 107.009583333333 1.00896077088607 0.9981617625119 32 102.74 103.364861723616 107.31 0.963236061165 0.993954795534996 33 112.63 111.185788806606 107.565416666667 1.03365739893109 1.01298917072852 34 106.26 107.323958250186 107.953333333333 0.9941699337694 0.990086479593813 35 108.86 109.130615841816 108.43875 1.00638024545484 0.997520257356483 36 111.38 110.368334954389 108.854166666667 1.01391006273888 1.00916626173648 37 106.85 107.310062918869 109.227916666667 0.982441725464283 0.995712770020303 38 107.86 107.553763707239 109.69625 0.98046891946843 1.00284728569420 39 107.94 108.521196612005 110.03875 0.986208918331083 0.994644395471583 40 111.38 110.852357221294 110.31125 1.00490527685339 1.00475986972160 41 111.29 110.544626520962 110.78625 0.99781901202507 1.00674273822705 42 113.72 114.154237752599 111.062083333333 1.02784167491245 0.996196043518418 43 111.88 112.282619788378 111.285416666667 1.00896077088607 0.996414228763662 44 109.87 107.503164651396 111.60625 0.963236061165 1.02201642487715 45 113.72 115.618456285689 111.85375 1.03365739893109 0.983579989331479 46 111.71 111.492844172459 112.146666666667 0.9941699337694 1.00194771089708 47 114.81 113.048370272351 112.331666666667 1.00638024545484 1.01558297322999 48 112.05 114.015031480040 112.450833333333 1.01391006273888 0.98276515425614 49 111.54 110.648318031853 112.625833333333 0.982441725464283 1.00805870332245 50 110.87 110.337069852380 112.535 0.98046891946843 1.00483001903470 51 110.87 110.690034391568 112.237916666667 0.986208918331083 1.00162585195155 52 115.48 112.486584427777 111.9375 1.00490527685339 1.02661131180621 53 111.63 111.271787125976 111.515 0.99781901202507 1.00321926054462 54 116.24 114.188927409127 111.095833333333 1.02784167491245 1.01796209700371 55 113.56 NA NA 1.00896077088607 NA 56 106.01 NA NA 0.963236061165 NA 57 110.45 NA NA 1.03365739893109 NA 58 107.77 NA NA 0.9941699337694 NA 59 108.61 NA NA 1.00638024545484 NA 60 108.19 NA NA 1.01391006273888 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/03/t12598607234gicc79acg1yz84/174fw1259860476.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t12598607234gicc79acg1yz84/174fw1259860476.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t12598607234gicc79acg1yz84/2iw881259860476.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t12598607234gicc79acg1yz84/2iw881259860476.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t12598607234gicc79acg1yz84/34izl1259860476.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t12598607234gicc79acg1yz84/34izl1259860476.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t12598607234gicc79acg1yz84/4gph51259860476.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t12598607234gicc79acg1yz84/4gph51259860476.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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