Decompositie van tijdreeksen - Geregistreerde nieuwe domeinnamen - Dorien Dhanis

*Unverified author*

R Software Module: rwasp_decompose.wasp (opens new window with default values)

Title produced by software: Classical Decomposition

Date of computation: Thu, 28 May 2009 09:52:19 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/May/28/t1243526215pfvxewkcnp765xg.htm/, Retrieved Thu, 28 May 2009 17:56:59 +0200

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/May/28/t1243526215pfvxewkcnp765xg.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 «

8166 2322 2924 5209 5597 5616 5764 5854 6019 6047 6082 6251 6576 6820 7024 7102 7107 7237 7630 7842 8086 8201 8323 9016 9077 9115 9230 9535 9565 9807 9815 9999 10176 10416 10439 10737 10790 11196 11221 11340 11356 11772 11836 11926 12013 12132 12178 12382 12448 12543 12662 12692 12767 13136 13145 13330 13381 13533 14176 14314 14444 15092 15130 15550 15557 15874 15892 16364 16379 16668 16713 16830 17368 17808 17846 18137 18504 18898 18938 19139 19573 19796 19845 21461

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 8166 NA NA -4.17476851851968 NA 2 2322 NA NA 123.915509259259 NA 3 2924 NA NA 27.3668981481487 NA 4 5209 NA NA 44.9293981481476 NA 5 5597 NA NA -62.7928240740737 NA 6 5616 NA NA 47.3391203703719 NA 7 5764 5434.6724537037 5421.33333333333 13.3391203703702 329.327546296296 8 5854 5589.89467592593 5542.5 47.3946759259257 264.105324074073 9 6019 5860.14467592593 5900.75 -40.6053240740741 158.855324074074 10 6047 6073.61689814815 6150.45833333333 -76.8414351851851 -26.6168981481478 11 6082 6188.33217592593 6292.25 -103.917824074074 -106.332175925925 12 6251 6406.75578703704 6422.70833333333 -15.9525462962966 -155.755787037036 13 6576 6563.82523148148 6568 -4.17476851851968 12.1747685185210 14 6820 6852.4988425926 6728.58333333333 123.915509259259 -32.4988425925922 15 7024 6924.90856481481 6897.54166666667 27.3668981481487 99.0914351851852 16 7102 7118.34606481481 7073.41666666667 44.9293981481476 -16.3460648148130 17 7107 7193.74884259259 7256.54166666667 -62.7928240740737 -86.7488425925922 18 7237 7512.46412037037 7465.125 47.3391203703719 -275.464120370370 19 7630 7697.88078703704 7684.54166666667 13.3391203703702 -67.8807870370356 20 7842 7931.76967592593 7884.375 47.3946759259257 -89.7696759259252 21 8086 8031.31134259259 8071.91666666667 -40.6053240740741 54.6886574074088 22 8201 8188.36689814815 8265.20833333333 -76.8414351851851 12.6331018518522 23 8323 8365.08217592593 8469 -103.917824074074 -42.0821759259252 24 9016 8662.5474537037 8678.5 -15.9525462962966 353.452546296299 25 9077 8872.45023148148 8876.625 -4.17476851851968 204.549768518522 26 9115 9181.45717592593 9057.54166666667 123.915509259259 -66.4571759259252 27 9230 9261.86689814815 9234.5 27.3668981481487 -31.866898148146 28 9535 9458.80439814815 9413.875 44.9293981481476 76.1956018518504 29 9565 9531.54050925926 9594.33333333333 -62.7928240740737 33.4594907407409 30 9807 9801.5474537037 9754.20833333333 47.3391203703719 5.45254629629562 31 9815 9910.63078703704 9897.29166666667 13.3391203703702 -95.6307870370383 32 9999 10102.7696759259 10055.375 47.3946759259257 -103.769675925923 33 10176 10184.4363425926 10225.0416666667 -40.6053240740741 -8.43634259259488 34 10416 10306.3668981481 10383.2083333333 -76.8414351851851 109.633101851850 35 10439 10429.1238425926 10533.0416666667 -103.917824074074 9.87615740740875 36 10737 10673.5891203704 10689.5416666667 -15.9525462962966 63.4108796296314 37 10790 10851.4502314815 10855.625 -4.17476851851968 -61.4502314814818 38 11196 11144.0405092593 11020.125 123.915509259259 51.9594907407409 39 11221 11204.3252314815 11176.9583333333 27.3668981481487 16.6747685185201 40 11340 11369.9293981481 11325 44.9293981481476 -29.929398148146 41 11356 11406.1655092593 11468.9583333333 -62.7928240740737 -50.1655092592591 42 11772 11657.2974537037 11609.9583333333 47.3391203703719 114.702546296296 43 11836 11760.9224537037 11747.5833333333 13.3391203703702 75.0775462962956 44 11926 11920.1863425926 11872.7916666667 47.3946759259257 5.81365740740694 45 12013 11948.3530092593 11988.9583333333 -40.6053240740741 64.6469907407427 46 12132 12028.4918981481 12105.3333333333 -76.8414351851851 103.508101851850 47 12178 12116.5405092593 12220.4583333333 -103.917824074074 61.4594907407427 48 12382 12320.1307870370 12336.0833333333 -15.9525462962966 61.8692129629635 49 12448 12443.2835648148 12447.4583333333 -4.17476851851968 4.71643518518795 50 12543 12684.4155092593 12560.5 123.915509259259 -141.415509259257 51 12662 12703.3668981481 12676 27.3668981481487 -41.3668981481478 52 12692 12836.3043981481 12791.375 44.9293981481476 -144.304398148148 53 12767 12870.2071759259 12933 -62.7928240740737 -103.207175925925 54 13136 13144.0891203704 13096.75 47.3391203703719 -8.08912037036862 55 13145 13273.7557870370 13260.4166666667 13.3391203703702 -128.755787037033 56 13330 13497.1863425926 13449.7916666667 47.3946759259257 -167.186342592588 57 13381 13618.2280092593 13658.8333333333 -40.6053240740741 -237.228009259255 58 13533 13803.9085648148 13880.75 -76.8414351851851 -270.908564814814 59 14176 14012.1655092593 14116.0833333333 -103.917824074074 163.834490740743 60 14314 14330.4641203704 14346.4166666667 -15.9525462962966 -16.4641203703686 61 14444 14570.7835648148 14574.9583333333 -4.17476851851968 -126.783564814816 62 15092 14939.7488425926 14815.8333333333 123.915509259259 152.251157407409 63 15130 15094.5335648148 15067.1666666667 27.3668981481487 35.4664351851879 64 15550 15367.6377314815 15322.7083333333 44.9293981481476 182.362268518518 65 15557 15496.2488425926 15559.0416666667 -62.7928240740737 60.7511574074069 66 15874 15816.9224537037 15769.5833333333 47.3391203703719 57.0775462962974 67 15892 16009.5891203704 15996.25 13.3391203703702 -117.589120370370 68 16364 16278.6446759259 16231.25 47.3946759259257 85.355324074073 69 16379 16416.9780092593 16457.5833333333 -40.6053240740741 -37.9780092592591 70 16668 16601.7002314815 16678.5416666667 -76.8414351851851 66.2997685185219 71 16713 16805.2071759259 16909.125 -103.917824074074 -92.207175925927 72 16830 17141.9641203704 17157.9166666667 -15.9525462962966 -311.964120370372 73 17368 17406.6585648148 17410.8333333333 -4.17476851851968 -38.6585648148175 74 17808 17777.2905092593 17653.375 123.915509259259 30.7094907407409 75 17846 17929.4502314815 17902.0833333333 27.3668981481487 -83.4502314814818 76 18137 18210.4293981481 18165.5 44.9293981481476 -73.429398148146 77 18504 18363.5405092593 18426.3333333333 -62.7928240740737 140.459490740741 78 18898 18797.1307870370 18749.7916666667 47.3391203703719 100.869212962964 79 18938 NA NA 13.3391203703702 NA 80 19139 NA NA 47.3946759259257 NA 81 19573 NA NA -40.6053240740741 NA 82 19796 NA NA -76.8414351851851 NA 83 19845 NA NA -103.917824074074 NA 84 21461 NA NA -15.9525462962966 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/28/t1243526215pfvxewkcnp765xg/12ad01243525937.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/28/t1243526215pfvxewkcnp765xg/12ad01243525937.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/28/t1243526215pfvxewkcnp765xg/2x36r1243525937.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/28/t1243526215pfvxewkcnp765xg/2x36r1243525937.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/28/t1243526215pfvxewkcnp765xg/3rkc81243525937.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/28/t1243526215pfvxewkcnp765xg/3rkc81243525937.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/28/t1243526215pfvxewkcnp765xg/4fa351243525937.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/28/t1243526215pfvxewkcnp765xg/4fa351243525937.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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