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Paper: Classical Decomposition - AEX

*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: Mon, 27 Dec 2010 23:38:26 +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/Dec/28/t12934932127evu732zk7jh5c0.htm/, Retrieved Tue, 28 Dec 2010 00:40:13 +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/2010/Dec/28/t12934932127evu732zk7jh5c0.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:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
508643 527568 520008 498484 523917 553522 558901 548933 567013 551085 588245 605010 631572 639180 653847 657073 626291 625616 633352 672820 691369 702595 692241 718722 732297 721798 766192 788456 806132 813944 788025 765985 702684 730159 678942 672527 594783 594575 576299 530770 524491 456590 428448 444937 372206 317272 297604 288561 289287 258923 255493 277992 295474 291680 318736 338463 351963 347240 347081 383486
 
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'RServer@AstonUniversity' @ vre.aston.ac.uk


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1508643NANA-6183.20225694447NA
2527568NANA-9854.83767361111NA
3520008NANA3916.4123263889NA
4498484NANA8894.90190972227NA
5523917NANA13054.6623263888NA
6553522NANA1734.82899305554NA
7558901554377.641493056551066.1253311.516493055534523.3585069445
8548933575220.776909722560838.66666666714382.1102430556-26287.7769097222
9567013566150.901909722571065.791666667-4914.8897569444862.098090277752
10551085575347.308159722583250.291666667-7902.98350694438-24262.3081597222
11588245579877.422743056594123.75-14246.32725694458367.5772569445
12605010599201.058159722601393.25-2192.191840277835808.94184027787
13631572601316.089409722607499.291666667-6183.2022569444730255.9105902779
14639180605908.537326389615763.375-9854.8376736111133271.4626736111
15653847630023.245659722626106.8333333333916.412326388923823.7543402779
16657073646496.151909722637601.258894.9019097222710576.8480902779
17626291661301.995659722648247.33333333313054.6623263888-35010.9956597222
18625616659053.328993056657318.51734.82899305554-33437.3289930555
19633352669564.891493056666253.3753311.51649305553-36212.8914930555
20672820688274.776909722673892.66666666714382.1102430556-15454.7769097222
21691369677101.235243056682016.125-4914.889756944414267.7647569445
22702595684268.474826389692171.458333333-7902.9835069443818326.5251736112
23692241690892.797743056705139.125-14246.32725694451348.2022569445
24718722718287.308159722720479.5-2192.19184027783434.691840277752
25732297728588.006076389734771.208333333-6183.202256944473708.99392361112
26721798735242.953993056745097.791666667-9854.83767361111-13444.9539930555
27766192753367.537326389749451.1253916.412326388912824.4626736112
28788456759965.985243055751071.0833333338894.9019097222728490.0147569446
29806132764720.120659722751665.45833333313054.662326388841411.8793402779
30813944750921.370659722749186.5416666671734.8289930555463022.6293402779
31788025744843.5164930567415323311.5164930555343181.4835069445
32765985744883.401909722730501.29166666714382.110243055621101.5980902779
33702684712373.235243056717288.125-4914.8897569444-9689.2352430555
34730159690736.016493056698639-7902.9835069443839422.9835069445
35678942661920.714409722676167.041666667-14246.327256944517021.2855902779
36672527647350.058159722649542.25-2192.1918402778325176.9418402779
37594783613486.922743056619670.125-6183.20225694447-18703.9227430555
38594575581455.912326389591310.75-9854.8376736111113119.0876736112
39576299568080.245659722564163.8333333333916.41232638898218.75434027775
40530770542085.193576389533190.2916666678894.90190972227-11315.1935763889
41524491513152.245659722500097.58333333313054.662326388811338.7543402778
42456590469944.745659722468209.9166666671734.82899305554-13354.7456597222
43428448442793.849826389439482.3333333333311.51649305553-14345.8498263888
44444937427149.943576389412767.83333333314382.110243055617787.0564236111
45372206380500.526909722385415.416666667-4914.8897569444-8294.52690972225
46317272353613.099826389361516.083333333-7902.98350694438-36341.0998263889
47297604327194.964409722341441.291666667-14246.3272569445-29590.9644097222
48288561322835.474826389325027.666666667-2192.19184027783-34274.4748263888
49289287307401.881076389313585.083333333-6183.20225694447-18114.8810763888
50258923294722.495659722304577.333333333-9854.83767361111-35799.4956597222
51255493303213.870659722299297.4583333333916.4123263889-47720.8706597222
52277992308597.568576389299702.6666666678894.90190972227-30605.5685763889
53295474316067.537326389303012.87513054.6623263888-20593.5373263889
54291680310764.453993056309029.6251734.82899305554-19084.4539930556
55318736NANA3311.51649305553NA
56338463NANA14382.1102430556NA
57351963NANA-4914.8897569444NA
58347240NANA-7902.98350694438NA
59347081NANA-14246.3272569445NA
60383486NANA-2192.19184027783NA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/28/t12934932127evu732zk7jh5c0/1h3b81293493103.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t12934932127evu732zk7jh5c0/1h3b81293493103.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t12934932127evu732zk7jh5c0/2acst1293493103.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t12934932127evu732zk7jh5c0/2acst1293493103.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t12934932127evu732zk7jh5c0/3acst1293493103.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t12934932127evu732zk7jh5c0/3acst1293493103.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t12934932127evu732zk7jh5c0/423re1293493103.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t12934932127evu732zk7jh5c0/423re1293493103.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')
 





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