Classical Decomposition (multiplicatief) - Verhandelde kapitalen Euronext

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

Title produced by software: Classical Decomposition

Date of computation: Mon, 24 May 2010 09:14:43 +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/24/t12746926980dwsc7bqbv2dsuf.htm/, Retrieved Mon, 24 May 2010 11:18:18 +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/24/t12746926980dwsc7bqbv2dsuf.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 «

3592,21 5955,74 4652,25 4211,65 4787,85 3599,73 4174,27 5106,33 5325,75 6604,61 5711,90 6919,30 7048,76 8655,98 6658,53 7247,03 8779,57 6602,49 9832,48 9369,49 8582,76 8206,94 6515,83 8618,10 8505,39 9881,64 9375,29 15642,50 12232,73 6288,93 12473,94 11142,82 10236,32 10581,51 8763,71 10819,04 11636,25 14650,13 10671,38 17468,63 13873,19 13077,58 16866,81 14186,64 19919,87 17681,78 9984,28 17423,09 13514,45 12334,57 12274,56 11752,23 13054,00 12460,98 8626,68 13722,62 12066,22 6798,83 6593,82 6606,19 6315,28 7232,95 6747,44 7803,61 6700,31 5369,53 8081,19 10718,39 9447,21 6815,10 5497,80 6805,31

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 3 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 3592.21 NA NA 0.939141670558009 NA 2 5955.74 NA NA 1.05581132590604 NA 3 4652.25 NA NA 0.904809104827505 NA 4 4211.65 NA NA 1.16452206257041 NA 5 4787.85 NA NA 1.07340443509932 NA 6 3599.73 NA NA 0.844802307861316 NA 7 4174.27 5409.88653126302 5197.48875 1.04086546243376 0.771600286970428 8 5106.33 5980.42090440669 5454.02166666667 1.09651579511633 0.853841239876174 9 5325.75 6269.53356358163 5650.12666666667 1.10962708156070 0.849465107091241 10 6604.61 5851.67421289182 5860.19583333333 0.998545847155305 1.12867014801497 11 5711.9 4776.58696079526 6152.99166666667 0.776303174059544 1.19581199858424 12 6919.3 6416.40623732229 6444.42833333333 0.995651732851757 1.07837623493234 13 7048.76 6391.14276719356 6805.30208333333 0.939141670558009 1.10289509353195 14 8655.98 7621.57729973299 7218.6925 1.05581132590604 1.13572029247847 15 6658.53 6815.05196086047 7532.03291666667 0.904809104827505 0.97703290279232 16 7247.03 9007.00220077866 7734.50541666667 1.16452206257041 0.804599559148935 17 8779.57 8409.87284071649 7834.76625 1.07340443509932 1.04395989883386 18 6602.49 6706.92494621869 7939.04666666667 0.844802307861316 0.984428788594456 19 9832.48 8400.32856773851 8070.52291666667 1.04086546243376 1.17048754947059 20 9369.49 8972.00474264342 8182.285 1.09651579511633 1.04430283629559 21 8582.76 9261.56069166818 8346.5525 1.10962708156070 0.92670774243494 22 8206.94 8796.75163356933 8809.56208333333 0.998545847155305 0.93295120083662 23 6515.83 7222.14638558533 9303.255 0.776303174059544 0.902201319680384 24 8618.1 9393.04980276433 9434.07166666667 0.995651732851757 0.917497530723593 25 8505.39 8951.02265415114 9531.0675 0.939141670558009 0.950214330655897 26 9881.64 10257.225067954 9715.01708333333 1.05581132590604 0.963383364851044 27 9375.29 8919.43096360652 9857.80416666667 0.904809104827505 1.05110853352120 28 15642.5 11675.0823679111 10025.6429166667 1.16452206257041 1.33981924127519 29 12232.73 10968.3095019315 10218.245 1.07340443509932 1.11527943279189 30 6288.93 8788.99585009484 10403.6125 0.844802307861316 0.715545906183599 31 12473.94 11059.9978721527 10625.7708333333 1.04086546243376 1.12784289329815 32 11142.82 12012.2323059594 10954.9104166667 1.09651579511633 0.927622752889312 33 10236.32 12436.2578663336 11207.60125 1.10962708156070 0.823102906840725 34 10581.51 11321.2070103812 11337.69375 0.998545847155305 0.934662707809964 35 8763.71 8913.61784548018 11482.135 0.776303174059544 0.983182154757039 36 10819.04 11781.8933586669 11833.3479166667 0.995651732851757 0.918276856753367 37 11636.25 11550.7331045932 12299.2445833333 0.939141670558009 1.00740359028578 38 14650.13 13312.8376282240 12609.1066666667 1.05581132590604 1.10045133946055 39 10671.38 11888.6611930957 13139.41375 0.904809104827505 0.897609901289588 40 17468.63 16115.5175629581 13838.7395833333 1.16452206257041 1.08396332489824 41 13873.19 15226.7155515909 14185.44125 1.07340443509932 0.911108502223944 42 13077.58 12259.3208824535 14511.4670833333 0.844802307861316 1.06674587649611 43 16866.81 15472.3549408164 14864.8941666667 1.04086546243376 1.09012558621603 44 14186.64 16279.6090736429 14846.6708333333 1.09651579511633 0.871436158928934 45 19919.87 16441.3315218356 14816.9883333333 1.10962708156070 1.21157279588606 46 17681.78 14624.3072197054 14645.6041666667 0.998545847155305 1.20906787134332 47 9984.28 11158.0290313800 14373.2879166667 0.776303174059544 0.894806777426456 48 17423.09 14251.2245709434 14313.4633333333 0.995651732851757 1.22256792132261 49 13514.45 13095.7980243424 13944.4329166667 0.939141670558009 1.03196842031920 50 12334.57 14339.7760337376 13581.76 1.05581132590604 0.860164759266818 51 12274.56 11975.3207931257 13235.1904166667 0.904809104827505 1.02498799088923 52 11752.23 14503.5385726306 12454.49875 1.16452206257041 0.810300875275877 53 13054 12730.3332952727 11859.7733333333 1.07340443509932 1.02542484137847 54 12460.98 9519.06344451974 11267.8 0.844802307861316 1.30905525240237 55 8626.68 10946.9178146199 10517.1304166667 1.04086546243376 0.788046475372166 56 13722.62 10970.1991825313 10004.5975 1.09651579511633 1.25089980333735 57 12066.22 10609.9582533283 9561.73333333333 1.10962708156070 1.13725423907439 58 6798.83 9153.58074386797 9166.91083333333 0.998545847155305 0.742750863322484 59 6593.82 6783.0638115231 8737.64791666667 0.776303174059544 0.97210054087924 60 6606.19 8141.87608346794 8177.43375 0.995651732851757 0.811384247595447 61 6315.28 7380.92843495447 7859.22791666666 0.939141670558009 0.855621356534526 62 7232.95 8141.70207313547 7711.32291666667 1.05581132590604 0.888383035270474 63 6747.44 6765.27690398874 7477.02125 0.904809104827505 0.997363462835022 64 7803.61 8580.86670155217 7368.57375 1.16452206257041 0.909419790729114 65 6700.31 7861.16772532318 7323.58416666667 1.07340443509932 0.852330116099202 66 5369.53 6155.4098395699 7286.21333333333 0.844802307861316 0.872326967650816 67 8081.19 NA NA 1.04086546243376 NA 68 10718.39 NA NA 1.09651579511633 NA 69 9447.21 NA NA 1.10962708156070 NA 70 6815.1 NA NA 0.998545847155305 NA 71 5497.8 NA NA 0.776303174059544 NA 72 6805.31 NA NA 0.995651732851757 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/24/t12746926980dwsc7bqbv2dsuf/1nw021274692479.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/24/t12746926980dwsc7bqbv2dsuf/1nw021274692479.ps (open in new window) http://www.freestatistics.org/blog/date/2010/May/24/t12746926980dwsc7bqbv2dsuf/2nw021274692479.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/24/t12746926980dwsc7bqbv2dsuf/2nw021274692479.ps (open in new window) http://www.freestatistics.org/blog/date/2010/May/24/t12746926980dwsc7bqbv2dsuf/3y5z51274692479.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/24/t12746926980dwsc7bqbv2dsuf/3y5z51274692479.ps (open in new window) http://www.freestatistics.org/blog/date/2010/May/24/t12746926980dwsc7bqbv2dsuf/48wgq1274692479.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/24/t12746926980dwsc7bqbv2dsuf/48wgq1274692479.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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