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Paper: Decomposition Loess - AEX

*The author of this computation has been verified*
R Software Module: /rwasp_decomposeloess.wasp (opens new window with default values)
Title produced by software: Decomposition by Loess
Date of computation: Tue, 28 Dec 2010 01:04:49 +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/t12934982353yx4zgqz85upxeh.htm/, Retrieved Tue, 28 Dec 2010 02:03:56 +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/t12934982353yx4zgqz85upxeh.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


Seasonal Decomposition by Loess - Parameters
ComponentWindowDegreeJump
Seasonal601061
Trend1912
Low-pass1312


Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
1508643522556.644549354-1583.81376149089496313.16921213713913.6445493536
2527568552375.754147494-2637.39089156443505397.6367440724807.7541474944
3520008520358.2509526775175.64477132048514482.104276003350.25095267687
4498484471161.2239212751945.70617417314523861.069904552-27322.7760787249
5523917507359.4083673137234.55609958649533240.035533101-16557.5916326873
6553522562402.0107778171856.57998156477542785.4092406198880.01077781664
7558901564780.01744136691.199610502914552330.7829481365879.01744136075
8548933523988.14606774811839.7927452326562038.06118702-24944.8539322524
9567013565208.105579335-2927.44500523815571745.339425903-1804.89442066511
10551085527855.912010773-7896.7707818405582210.858771068-23229.0879892274
11588245598150.552294221-14336.9304104532592676.3781162339905.55229422066
12605010607776.112763144638.898954793995601604.9882820622766.11276314361
13631572654194.215313599-1583.81376149089610533.59844789222622.2153135987
14639180661802.75081208-2637.39089156443619194.64007948422622.7508120801
15653847674662.6735176035175.64477132048627855.68171107620815.6735176031
16657073675113.6892513131945.70617417314637086.60457451418040.6892513129
17626291599029.9164624627234.55609958649646317.527437952-27261.0835375381
18625616594289.158046871856.57998156477655086.261971566-31326.8419531304
19633352602157.803884317691.199610502914663854.99650518-31194.1961156826
20672820660694.28680992111839.7927452326673105.920444847-12125.7131900792
21691369703308.600620725-2927.44500523815682356.84438451311939.6006207247
22702595718667.890149673-7896.7707818405694418.88063216816072.8901496726
23692241692338.013530631-14336.9304104532706480.91687982297.0135306308512
24718722717134.427026985638.898954793995719670.674018221-1587.57297301537
25732297733317.38260487-1583.81376149089732860.431156621020.3826048706
26721798704737.114460442-2637.39089156443741496.276431122-17060.8855395576
27766192777076.2335230565175.64477132048750132.12170562410884.2335230558
28788456822692.6115406741945.70617417314752273.68228515234236.6115406745
29806132850614.2010357337234.55609958649754415.24286468144482.2010357325
30813944876536.4750172981856.57998156477749494.94500113762592.475017298
31788025830784.153251904691.199610502914744574.64713759342759.1532519036
32765985788807.7964010511839.7927452326731322.41085371722822.7964010508
33702684690225.270435398-2927.44500523815718070.17456984-12458.7295646017
34730159771593.77034751-7896.7707818405696621.0004343341434.7703475103
35678942697049.104111633-14336.9304104532675171.82629882118107.1041116326
36672527696224.160763193638.898954793995648190.94028201323697.1607631932
37594783569939.759496286-1583.81376149089621210.054265205-24843.2405037141
38594575599910.476689681-2637.39089156443591876.9142018835335.47668968141
39576299584878.5810901185175.64477132048562543.7741385618579.58109011839
40530770527883.6682675111945.70617417314531710.625558316-2886.3317324887
41524491540869.9669223447234.55609958649500877.4769780716378.9669223437
42456590440803.1189992611856.57998156477470520.301019174-15786.8810007388
43428448416041.675329219691.199610502914440163.125060278-12406.3246707811
44444937465379.32534772811839.7927452326412654.8819070420442.3253477275
45372206362192.806251437-2927.44500523815385146.638753802-10013.1937485634
46317272279766.347861251-7896.7707818405362674.42292059-37505.6521387493
47297604269342.723323075-14336.9304104532340202.207087378-28261.2766769248
48288561251194.286847908638.898954793995325288.814197298-37366.7131520921
49289287269782.392454273-1583.81376149089310375.421307218-19504.6075457274
50258923216324.990145465-2637.39089156443304158.400746099-42598.0098545348
51255493207868.9750436995175.64477132048297941.38018498-47624.0249563005
52277992250405.2736976591945.70617417314303633.020128168-27586.7263023411
53295474274388.7838290587234.55609958649309324.660071356-21085.2161709423
54291680265688.2490484461856.57998156477315815.170969989-25991.7509515538
55318736314475.118520875691.199610502914322305.681868622-4260.88147912524
56338463335387.94888540311839.7927452326329698.258369365-3075.05111459712
57351963369762.610135131-2927.44500523815337090.83487010717799.6101351315
58347240356949.942177691-7896.7707818405345426.8286041499709.94217769144
59347081354736.108072262-14336.9304104532353762.8223381917655.10807226179
60383486403615.894613786638.898954793995362717.2064314220129.8946137862
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/28/t12934982353yx4zgqz85upxeh/1reuy1293498285.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t12934982353yx4zgqz85upxeh/1reuy1293498285.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t12934982353yx4zgqz85upxeh/2reuy1293498285.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t12934982353yx4zgqz85upxeh/2reuy1293498285.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t12934982353yx4zgqz85upxeh/32nbj1293498285.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t12934982353yx4zgqz85upxeh/32nbj1293498285.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t12934982353yx4zgqz85upxeh/42nbj1293498285.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t12934982353yx4zgqz85upxeh/42nbj1293498285.ps (open in new window)


 
Parameters (Session):
par1 = 12 ; par2 = periodic ; par3 = 0 ; par5 = 1 ; par7 = 1 ; par8 = FALSE ;
 
Parameters (R input):
par1 = 12 ; par2 = periodic ; par3 = 0 ; par5 = 1 ; par7 = 1 ; par8 = FALSE ;
 
R code (references can be found in the software module):
par1 <- as.numeric(par1) #seasonal period
if (par2 != 'periodic') par2 <- as.numeric(par2) #s.window
par3 <- as.numeric(par3) #s.degree
if (par4 == '') par4 <- NULL else par4 <- as.numeric(par4)#t.window
par5 <- as.numeric(par5)#t.degree
if (par6 != '') par6 <- as.numeric(par6)#l.window
par7 <- as.numeric(par7)#l.degree
if (par8 == 'FALSE') par8 <- FALSE else par9 <- TRUE #robust
nx <- length(x)
x <- ts(x,frequency=par1)
if (par6 != '') {
m <- stl(x,s.window=par2, s.degree=par3, t.window=par4, t.degre=par5, l.window=par6, l.degree=par7, robust=par8)
} else {
m <- stl(x,s.window=par2, s.degree=par3, t.window=par4, t.degre=par5, l.degree=par7, robust=par8)
}
m$time.series
m$win
m$deg
m$jump
m$inner
m$outer
bitmap(file='test1.png')
plot(m,main=main)
dev.off()
mylagmax <- nx/2
bitmap(file='test2.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(x),lag.max = mylagmax,main='Observed')
acf(as.numeric(m$time.series[,'trend']),na.action=na.pass,lag.max = mylagmax,main='Trend')
acf(as.numeric(m$time.series[,'seasonal']),na.action=na.pass,lag.max = mylagmax,main='Seasonal')
acf(as.numeric(m$time.series[,'remainder']),na.action=na.pass,lag.max = mylagmax,main='Remainder')
par(op)
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
spectrum(as.numeric(x),main='Observed')
spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'trend']),'trend']),main='Trend')
spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'seasonal']),'seasonal']),main='Seasonal')
spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'remainder']),'remainder']),main='Remainder')
par(op)
dev.off()
bitmap(file='test4.png')
op <- par(mfrow = c(2,2))
cpgram(as.numeric(x),main='Observed')
cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'trend']),'trend']),main='Trend')
cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'seasonal']),'seasonal']),main='Seasonal')
cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'remainder']),'remainder']),main='Remainder')
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Seasonal Decomposition by Loess - Parameters',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Component',header=TRUE)
a<-table.element(a,'Window',header=TRUE)
a<-table.element(a,'Degree',header=TRUE)
a<-table.element(a,'Jump',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Seasonal',header=TRUE)
a<-table.element(a,m$win['s'])
a<-table.element(a,m$deg['s'])
a<-table.element(a,m$jump['s'])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Trend',header=TRUE)
a<-table.element(a,m$win['t'])
a<-table.element(a,m$deg['t'])
a<-table.element(a,m$jump['t'])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Low-pass',header=TRUE)
a<-table.element(a,m$win['l'])
a<-table.element(a,m$deg['l'])
a<-table.element(a,m$jump['l'])
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Observed',header=TRUE)
a<-table.element(a,'Fitted',header=TRUE)
a<-table.element(a,'Seasonal',header=TRUE)
a<-table.element(a,'Trend',header=TRUE)
a<-table.element(a,'Remainder',header=TRUE)
a<-table.row.end(a)
for (i in 1:nx) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]+m$time.series[i,'remainder'])
a<-table.element(a,m$time.series[i,'seasonal'])
a<-table.element(a,m$time.series[i,'trend'])
a<-table.element(a,m$time.series[i,'remainder'])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')
 





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