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Classial decomposition bij Time series analysis -inschrijvingen nieuwe personenwagens (eigen reeks)-Ling Weng

*Unverified author*
R Software Module: /rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Tue, 17 May 2011 09:28:21 +0000
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2011/May/17/t1305624371jtadwqc7norausm.htm/, Retrieved Tue, 17 May 2011 11:26:11 +0200
 
Original text written by user:
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
KDGP2W92
 
Dataseries X:
» Textbox « » Textfile « » CSV «
26281 23899 25727 30733 28599 16723 43738 45272 46532 41032 37967 35366 33892 21560 26588 33527 24859 17952 45504 40129 40357 41913 33730 37842 33025 24050 30429 34507 25189 20253 48527 44446 46380 48950 38883 42928 37107 30186 32602 39892 32194 21629 59968 45694 55756 48554 41052 49822 39191 31994 35735 38930 33658 23849 58972 59249 63955 53785 52760 44795 37348 32370 32717 40974 33591 21124 58608 46865 51378 46235 47206 45382 41227 33795 31295 42625 33625 21538 56421 53152 53536 52408 41454 38271 35306 26414 31917 38030 27534 18387
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
126281NANA0.94688327302065NA
223899NANA0.733107003988366NA
325727NANA0.802457705665263NA
430733NANA0.973132829542438NA
528599NANA0.767614101714788NA
616723NANA0.531383939979044NA
74373846501.762821412233806.20833333331.375539142482330.940566493532163
84527241224.351319471334025.8751.211558889212141.09818586711435
94653244037.621013675733964.29166666671.296585880426151.05664200129134
104103241661.335289396734116.58333333331.22114617640190.984894020198222
113796736107.385180447434077.16666666671.059577092592231.05150233976399
123536636724.791967351633972.54166666671.08101396497470.963000689872947
133389232286.194587942734097.33333333330.946883273020651.04973659585936
142156024893.839619306433956.6250.7331070039883660.866077725642577
152658826870.329709939133485.04166666670.8024577056652630.989492882559061
163352732370.736461113233264.45833333330.9731328295424381.03571940787556
172485925426.929264014233124.6250.7676141017147880.977664260669574
181795217562.903446232433051.250.5313839399790441.02215445498285
194550445555.506519646233118.29166666671.375539142482330.998869367863926
204012940206.692334153433185.91666666671.211558889212140.998067676557232
214035743370.419529373133449.70833333331.296585880426150.930519013602527
224191341092.281171193433650.58333333331.22114617640191.01997257892273
233373035713.222502003133705.16666666671.059577092592230.944468116762864
243784236554.262014376833814.79166666671.08101396497471.03522812155575
253302532228.710882576534036.6250.946883273020651.02470744549246
262405025176.696738345334342.45833333330.7331070039883660.955248428733335
273042927904.095849262434773.29166666670.8024577056652631.09048507302932
283450734368.578160163835317.45833333330.9731328295424381.00402756957798
292518927500.063049220435825.3750.7676141017147880.915961536339607
302025319263.7305921203362520.5313839399790441.05135398894565
314852750391.5009456975366341.375539142482330.962999694180438
324444644900.069544479837059.751.211558889212140.989887108214166
334638048500.037418809137405.95833333331.296585880426150.9562879219968
344895046062.70227678437720.8751.22114617640191.06268190055083
353888340515.181736585638237.1251.059577092592230.959714317778521
364292841712.365190502138586.33333333331.08101396497471.02914327211957
373710737042.428721795239120.3750.946883273020651.0017431707486
383018629067.02069338539649.08333333330.7331070039883661.03849652561295
393260232171.933721105340091.750.8024577056652631.01336774726141
403989239378.711985861840465.91666666670.9731328295424381.01303465726158
413219431118.91576391340539.79166666670.7676141017147881.0345476122704
422162921742.858082097540917.41666666670.5313839399790440.994763426148135
435996856798.07450180941291.51.375539142482331.05581043945583
444569450223.558340437241453.66666666671.211558889212140.909812078432718
455575654015.173510025141659.54166666671.296585880426151.03222847168401
464855450982.8528647792417501.22114617640190.952359416386109
474105244259.506436578941770.91666666671.059577092592230.927529548003997
484982245320.879890084841924.41666666671.08101396497471.09931669731108
493919139745.819919738841975.41666666670.946883273020650.986040798230878
503199431156.100739625442498.70833333330.7331070039883661.0268935855413
513573534830.77702161443405.1250.8024577056652631.0259604595621
523893042783.501020424743964.70833333330.9731328295424380.909930208409428
533365834289.705730650444670.50.7676141017147880.981577394229844
542384923885.110295125544948.8750.5313839399790440.99848816711837
555897261435.188893509744662.6251.375539142482330.95990589533664
565924954037.343797195444601.51.211558889212141.09644545487588
576395557686.942650156944491.41666666671.296585880426151.10865643180045
585378554280.96516287844450.83333333331.22114617640190.990863000291358
595276047186.367409637344533.20833333331.059577092592231.11811955224221
604479548015.262155535744416.8751.08101396497470.932932529971319
613734841935.724209417344288.16666666670.946883273020650.890601049680046
623237032078.5631735189437570.7331070039883661.00908509601582
633271734278.552377165342716.95833333330.8024577056652630.954445206437436
644097440753.181013188141878.33333333330.9731328295424381.0054184478689
653359131727.281923442941332.33333333330.7676141017147881.05874181346685
662112421853.363800615741125.3750.5313839399790440.966624643818215
675860856825.527970527741311.45833333331.375539142482331.03136745214927
684686550319.01908458341532.45833333331.211558889212140.93135758312822
695137853850.561127622641532.58333333331.296585880426150.954084765769426
704623550729.007103359741542.1251.22114617640190.91141149097984
714720644091.475169311941612.33333333331.059577092592231.07063780058908
724538245003.6923758618416311.08101396497471.00840614634414
734122739349.746537328241557.1250.946883273020651.04770687559298
743379530591.058516301441727.95833333330.7331070039883661.10473457405834
753129533767.286511443342079.83333333330.8024577056652630.926784566755001
764262541287.066011795842426.95833333330.9731328295424381.03240564461088
773362532580.996740233342444.50.7676141017147881.03204331862805
782153822269.525989609341908.54166666670.5313839399790440.967151254591114
795642156899.921712483641365.54166666671.375539142482330.991583086618228
805315249445.283198979540811.29166666671.211558889212141.07496603439612
815353652550.193538378640529.66666666671.296585880426151.01875933075111
825240849290.496907558340364.1251.22114617640191.06324754847346
834145442297.125512052539918.8751.059577092592230.980066600227661
843827142736.580880067139533.79166666671.08101396497470.895509168302467
8535306NANANANA
8626414NANANANA
8731917NANANANA
8838030NANANANA
8927534NANANANA
9018387NANANANA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2011/May/17/t1305624371jtadwqc7norausm/1iyep1305624498.png (open in new window)
http://www.freestatistics.org/blog/date/2011/May/17/t1305624371jtadwqc7norausm/1iyep1305624498.ps (open in new window)


http://www.freestatistics.org/blog/date/2011/May/17/t1305624371jtadwqc7norausm/27hfc1305624498.png (open in new window)
http://www.freestatistics.org/blog/date/2011/May/17/t1305624371jtadwqc7norausm/27hfc1305624498.ps (open in new window)


http://www.freestatistics.org/blog/date/2011/May/17/t1305624371jtadwqc7norausm/3ip5x1305624498.png (open in new window)
http://www.freestatistics.org/blog/date/2011/May/17/t1305624371jtadwqc7norausm/3ip5x1305624498.ps (open in new window)


http://www.freestatistics.org/blog/date/2011/May/17/t1305624371jtadwqc7norausm/49oux1305624498.png (open in new window)
http://www.freestatistics.org/blog/date/2011/May/17/t1305624371jtadwqc7norausm/49oux1305624498.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')
 





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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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