Time Series Analysis Champagne Classical Decompostion

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Tue, 25 May 2010 15:58:18 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/May/25/t1274803240ks4ejk6ktgef2hi.htm/, Retrieved Tue, 25 May 2010 18:00:40 +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/2010/May/25/t1274803240ks4ejk6ktgef2hi.htm/}, year = {2010}, } @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 = {2010}, 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:

KDGP2W52

Dataseries X:

» Textbox « » Textfile « » CSV «

2851 2672 2755 2721 2946 3036 2282 2212 2922 4301 5764 7132 2541 2475 3031 3266 3776 3230 3028 1759 3595 4474 6838 8357 3113 3006 4047 3523 3937 3986 3260 1573 3528 5211 7614 9254 5375 3088 3718 4514 4520 4539 3663 1643 4739 5428 8314 10651 3633 4292 4154 4121 4647 4753 3965 1723 5048 6922 9858 11331 4016 3975 4510 4276 4968 4677 3523 1821 5222 6873 10803 13916 2639 2899 3370 3740 2927 3986 4217 1738 5221 6424 9842 13076 3934 3162 4286 4676 5010 4874 4633 1659 5951 6981 9851 12670

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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 2851 NA NA 0.75498520482829 NA 2 2672 NA NA 0.682668841284181 NA 3 2755 NA NA 0.809272328644709 NA 4 2721 NA NA 0.831010018388069 NA 5 2946 NA NA 0.87544764499555 NA 6 3036 NA NA 0.867001671303797 NA 7 2282 2521.51343415434 3453.25 0.730185603172182 0.905012033285214 8 2212 1353.72287373660 3432.125 0.394427030990013 1.63401242818211 9 2922 3133.6497451028 3435.41666666667 0.912160022831623 0.932459029464424 10 4301 4143.20603025629 3469.625 1.19413655085385 1.03808499229616 11 5764 6212.94022552641 3526.91666666667 1.76157840196387 0.927741100150634 12 7132 7807.13094747195 3569.58333333333 2.18712668074386 0.913523808936422 13 2541 2724.55285792409 3608.75 0.75498520482829 0.9326300984067 14 2475 2471.91542975496 3620.95833333333 0.682668841284181 1.00124784618758 15 3031 2937.75971202137 3630.125 0.809272328644709 1.03173856854156 16 3266 3045.96334614917 3665.375 0.831010018388069 1.07223877271176 17 3776 3254.33071233013 3717.33333333333 0.87544764499555 1.16030002288746 18 3230 3305.98574789029 3813.125 0.867001671303797 0.977015706150947 19 3028 2838.96162513344 3888 0.730185603172182 1.06658715397665 20 1759 1551.65950545509 3933.95833333333 0.394427030990013 1.1336249955715 21 3595 3647.19583795701 3998.41666666667 0.912160022831623 0.985688775630364 22 4474 4837.99448009475 4051.45833333333 1.19413655085385 0.924763353577116 23 6838 7167.64232029075 4068.875 1.76157840196387 0.954009658188778 24 8357 8982.71153837178 4107.08333333333 2.18712668074386 0.930342688207352 25 3113 3131.86737592895 4148.25 0.75498520482829 0.993975678512454 26 3006 2833.18946946956 4150.16666666667 0.682668841284181 1.06099504900489 27 4047 3350.08396346585 4139.625 0.809272328644709 1.20802942377992 28 3523 3463.26887704971 4167.54166666667 0.831010018388069 1.01724703598560 29 3937 3703.65421612409 4230.58333333333 0.87544764499555 1.06300420348639 30 3986 3728.36006209379 4300.29166666667 0.867001671303797 1.06910275124059 31 3260 3236.12174445885 4431.91666666667 0.730185603172182 1.00737866416245 32 1573 1786.59010578851 4529.58333333333 0.394427030990013 0.880448176055332 33 3528 4122.31718984943 4519.29166666667 0.912160022831623 0.855829340034085 34 5211 5429.5896296636 4546.875 1.19413655085385 0.959741040378195 35 7614 8125.20697995828 4612.45833333333 1.76157840196387 0.937083820606758 36 9254 10191.5546808746 4659.79166666667 2.18712668074386 0.908006706510244 37 5375 3548.14734324115 4699.625 0.75498520482829 1.51487508269317 38 3088 3221.74181830048 4719.33333333333 0.682668841284181 0.958487729357833 39 3718 3862.42078685867 4772.70833333333 0.809272328644709 0.962608738190815 40 4514 4015.61353593831 4832.20833333333 0.831010018388069 1.12411215860324 41 4520 4263.79480098041 4870.41666666667 0.87544764499555 1.06008853872627 42 4539 4298.41366097604 4957.79166666667 0.867001671303797 1.05597095998651 43 3663 3609.61168048142 4943.41666666667 0.730185603172182 1.01479059916812 44 1643 1940.97541950185 4921 0.394427030990013 0.846481610994162 45 4739 4551.07040724791 4989.33333333333 0.912160022831623 1.04129349272488 46 5428 5960.08479238042 4991.125 1.19413655085385 0.91072529822719 47 8314 8772.73384088016 4980.04166666667 1.76157840196387 0.947709135008462 48 10651 10923.0574253050 4994.25 2.18712668074386 0.9750932898444 49 3633 3786.81704111749 5015.75 0.75498520482829 0.959380915569107 50 4292 3434.96205306157 5031.66666666667 0.682668841284181 1.24950434202746 51 4154 4085.10555595741 5047.875 0.809272328644709 1.01686478919550 52 4121 4257.26432420207 5123 0.831010018388069 0.967992514952049 53 4647 4595.73536637456 5249.58333333333 0.87544764499555 1.01115482714704 54 4753 4631.73967852271 5342.25 0.867001671303797 1.02618029723034 55 3965 3933.17517588709 5386.54166666667 0.730185603172182 1.00809138232846 56 1723 2125.68231122255 5389.29166666667 0.394427030990013 0.810563267569858 57 5048 4917.37866975004 5390.91666666667 0.912160022831623 1.02656320349163 58 6922 6462.91579166913 5412.20833333333 1.19413655085385 1.07103360512954 59 9858 9568.96727856783 5432.04166666667 1.76157840196387 1.03020521577909 60 11331 11902.8901782783 5442.25 2.18712668074386 0.951953670939353 61 4016 4092.52313363922 5420.66666666667 0.75498520482829 0.98130172239951 62 3975 3690.73531226271 5406.33333333333 0.682668841284181 1.07702115261227 63 4510 4384.36771915415 5417.66666666667 0.809272328644709 1.02865459489107 64 4276 4506.4634534662 5422.875 0.831010018388069 0.948859353715843 65 4968 4780.12652660175 5460.20833333333 0.87544764499555 1.03930303358138 66 4677 4861.53124648785 5607.29166666667 0.867001671303797 0.962042567016068 67 3523 4131.11632314702 5657.625 0.730185603172182 0.852796126862929 68 1821 2191.20650174577 5555.41666666667 0.394427030990013 0.831049012746713 69 5222 4983.20621806439 5463.08333333333 0.912160022831623 1.04791970700911 70 6873 6440.27695289252 5393.25 1.19413655085385 1.06719013021841 71 10803 9311.48323548078 5285.875 1.76157840196387 1.16018036297761 72 13916 11311.9103230856 5172.04166666667 2.18712668074386 1.23020777238659 73 2639 3904.90931023939 5172.16666666667 0.75498520482829 0.675815951238626 74 2899 3548.25663617969 5197.625 0.682668841284181 0.817020947819962 75 3370 4203.4616340217 5194.125 0.809272328644709 0.80172017575327 76 3740 4300.78847391515 5175.375 0.831010018388069 0.869607985299345 77 2927 4479.33730657536 5116.625 0.87544764499555 0.653444873576135 78 3986 4371.06117601737 5041.58333333333 0.867001671303797 0.911906706286776 79 4217 3695.13466925296 5060.54166666667 0.730185603172182 1.14123039549531 80 1738 2021.61931287969 5125.45833333333 0.394427030990013 0.85970686415946 81 5221 4720.04805147747 5174.58333333333 0.912160022831623 1.10613280692465 82 6424 6271.3066309467 5251.75 1.19413655085385 1.02434793545253 83 9842 9472.9612556608 5377.54166666667 1.76157840196387 1.03895706256781 84 13076 12032.1129129989 5501.33333333333 2.18712668074386 1.08675841845478 85 3934 4194.44613629103 5555.66666666667 0.75498520482829 0.937906906459565 86 3162 3802.26633420751 5569.70833333333 0.682668841284181 0.831609288269134 87 4286 4529.36234470299 5596.83333333333 0.809272328644709 0.946270064926998 88 4676 4695.58748348435 5650.45833333333 0.831010018388069 0.995828534011294 89 5010 4967.32641468996 5674.04166666667 0.87544764499555 1.00859085587447 90 4874 4905.06195540123 5657.5 0.867001671303797 0.993667367367903 91 4633 NA NA 0.730185603172182 NA 92 1659 NA NA 0.394427030990013 NA 93 5951 NA NA 0.912160022831623 NA 94 6981 NA NA 1.19413655085385 NA 95 9851 NA NA 1.76157840196387 NA 96 12670 NA NA 2.18712668074386 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/25/t1274803240ks4ejk6ktgef2hi/1f5q41274803095.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/25/t1274803240ks4ejk6ktgef2hi/1f5q41274803095.ps (open in new window) http://www.freestatistics.org/blog/date/2010/May/25/t1274803240ks4ejk6ktgef2hi/2f5q41274803095.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/25/t1274803240ks4ejk6ktgef2hi/2f5q41274803095.ps (open in new window) http://www.freestatistics.org/blog/date/2010/May/25/t1274803240ks4ejk6ktgef2hi/37w771274803095.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/25/t1274803240ks4ejk6ktgef2hi/37w771274803095.ps (open in new window) http://www.freestatistics.org/blog/date/2010/May/25/t1274803240ks4ejk6ktgef2hi/47w771274803095.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/25/t1274803240ks4ejk6ktgef2hi/47w771274803095.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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