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Paper

*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: Sun, 12 Dec 2010 17:47:32 +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/12/t12921759325dk9blonbfae1fx.htm/, Retrieved Sun, 12 Dec 2010 18:45:37 +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/12/t12921759325dk9blonbfae1fx.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 «
25.2609 16.8622 13.3181 12.5621 14.2754 12.2961 10.0871 13.5117 13.9921 13.6932 14.4211 15.3397 16.5182 15.2809 15.6204 15.5698 15.9458 16.4063 17.55 17.0353 16.0591 16.3643 14.6527 13.4664 13.3266 13.1823 12.113 13.354 13.4537 13.2715 13.1959 13.5542 12.124 10.967 10.9201 12.5971 14.3177 14.2471 16.0926 17.1334 16.5866 16.361 15.8494 15.5932 16.6387 16.8312 16.5044 16.5556 16.7469 15.9543 15.5888 14.3945 13.8889 12.9999 14.1022 19.6245 24.7658 25.9843 22.9635 19.6288 17.3363 13.311 14.6359 15.834 16.2415 15.9808 16.9726 16.8708 16.923 18.1138 16.7716 14.0299 13.822 14.2537 14.3985 15.2454 15.6683 16.1721 14.8679 14.1948 14.7056 15.3819 15.5001 14.7886 14.563 15.5528 15.9781 15.5139 15.3603 15.0512 14.7874 14.9624 13.9188 14.5146 13.7115 12.0738 12.5688 12.2547 11.8741 13.0261 13.8681 14.2137 14.4743 13.9764 13.1558 13.0991 13.7831 13.2546 13.3426 13.5011 12.8245 13.6596 13.8754 12.9011 11.871 12 etc...
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'George Udny Yule' @ 72.249.76.132


Seasonal Decomposition by Loess - Parameters
ComponentWindowDegreeJump
Seasonal29610297
Trend1912
Low-pass1312


Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
125.260934.7548426479062-1.2878086772163117.05476602931019.49394264790624
216.862218.6809405199992-1.527210848767616.57067032876841.81874051999921
313.318111.3818397937529-0.8322144219796716.0865746282267-1.93626020624706
412.56219.47939192367384-0.054307539644105915.6991156159703-3.08270807632616
514.275412.59554320747880.6436001888073515.3116566037138-1.67985679252116
612.29618.542603770896531.0743778924777714.9752183366257-3.75349622910347
710.08714.518346531130531.0170733993318914.6387800695376-5.56875346886947
813.511711.21891095916721.4560895206130314.3483995202198-2.29278904083281
913.992112.60922806047971.3169529686182714.0580189709020-1.38287193952025
1013.693212.56653852359780.5856487262422214.23421275016-1.12666147640221
1114.421114.9583285049365-0.52653503435453814.4104065294180.537228504936536
1215.339717.7460620497988-1.8656657331865614.79900368338782.40636204979877
1316.518219.1366078398587-1.2878086772163115.18760083735762.61840783985872
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1816.406315.92636023229851.0743778924777715.8118618752237-0.479939767701479
1917.5518.38546928132191.0170733993318915.69745731934620.835469281321934
2017.035317.10693430389851.4560895206130315.50757617548840.0716343038985414
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2513.326613.6074125292845-1.2878086772163114.33359614793180.280812529284496
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3510.92018.88146800437149-0.52653503435453813.4852670299830-2.03863199562851
3612.597113.2250175691544-1.8656657331865613.83484816403220.627917569154357
3714.317715.7387793791350-1.2878086772163114.18442929808141.42107937913495
3814.247115.4968393968647-1.527210848767614.52457145190291.24973939686469
3916.092618.1527008162552-0.8322144219796714.86471360572452.06010081625522
4017.133419.1297025314069-0.054307539644105915.19140500823721.99630253140694
4116.586617.01150340044280.6436001888073515.51809641074990.424903400442775
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4516.638715.74485563246661.3169529686182716.2155913989151-0.893844367533385
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5214.394512.2648052945705-0.054307539644105916.5785022450736-2.12969470542947
5313.888910.14681472879150.6436001888073516.9873850824012-3.74208527120854
5412.99997.542040203549961.0743778924777717.3833819039723-5.45785979645004
5514.10229.407947875124761.0170733993318917.7793787255433-4.69425212487524
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6415.83414.546561899172-0.054307539644105917.1757456404721-1.28743810082801
6516.241515.20740533346130.6436001888073516.6319944777313-1.03409466653867
6615.980814.56141495411.0743778924777716.3258071534222-1.41938504590000
6716.972616.90850677155501.0170733993318916.0196198291131-0.0640932284450333
6816.870816.33174233977521.4560895206130315.9537681396118-0.539057660224817
6916.92316.64113058127131.3169529686182715.8879164501104-0.281869418728698
7018.113819.77402931583990.5856487262422215.86792195791791.66022931583991
7116.771618.2218075686292-0.52653503435453815.84792746572531.45020756862922
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7313.82213.2522495200007-1.2878086772163115.6795591572156-0.569750479999291
7414.253714.5469085256589-1.527210848767615.48770232310870.293208525658878
7514.398514.3333689329778-0.8322144219796715.2958454890018-0.0651310670221736
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7715.668315.76197531874050.6436001888073514.93102449245220.0936753187404786
7816.172116.37770854080921.0743778924777714.8921135667130.20560854080923
7914.867913.86552395969431.0170733993318914.8532026409738-1.00237604030571
8014.194811.99228387645441.4560895206130314.9412266029326-2.20251612354565
8114.705613.06499646649031.3169529686182715.0292505648914-1.64060353350968
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8414.788616.1804995297739-1.8656657331865615.26236620341271.39189952977388
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9414.514614.61228757025710.5856487262422213.83126370350070.0976875702571238
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18532.692635.39359894320630.6436001888073529.34800086798642.70099894320629
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29054.489154.6269391986213-1.527210848767655.87847165014630.137839198621307
29159.066561.9528789502755-0.8322144219796757.01233547170422.8863789502755
29263.992969.8887635550934-0.054307539644105958.15134398455075.89586355509345
29361.616763.29944731379550.6436001888073559.29035249739711.68274731379551
29462.181662.88262799744181.0743778924777760.40619411008040.701027997441834
29558.917855.29649087790451.0170733993318961.5220357227636-3.62130912209553
29659.915155.7806849900321.4560895206130362.593425489355-4.134415009968
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/12/t12921759325dk9blonbfae1fx/1oc6f1292176043.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/12/t12921759325dk9blonbfae1fx/1oc6f1292176043.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/12/t12921759325dk9blonbfae1fx/2zl501292176043.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/12/t12921759325dk9blonbfae1fx/2zl501292176043.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/12/t12921759325dk9blonbfae1fx/3zl501292176043.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/12/t12921759325dk9blonbfae1fx/3zl501292176043.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/12/t12921759325dk9blonbfae1fx/4ad431292176043.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/12/t12921759325dk9blonbfae1fx/4ad431292176043.ps (open in new window)


 
Parameters (Session):
par1 = 12 ;
 
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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Software written by Ed van Stee & Patrick Wessa


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