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Ws9forcasting2

*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: Fri, 04 Dec 2009 09:20:55 -0700
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/04/t12599437160pa8kai2x2lffdz.htm/, Retrieved Fri, 04 Dec 2009 17:22:02 +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/2009/Dec/04/t12599437160pa8kai2x2lffdz.htm/},
    year = {2009},
}
@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 = {2009},
    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:
ShwWs9forcasting2
 
Dataseries X:
» Textbox « » Textfile « » CSV «
58608 46865 51378 46235 47206 45382 41227 33795 31295 42625 33625 21538 56421 53152 53536 52408 41454 38271 35306 26414 31917 38030 27534 18387 50556 43901 48572 43899 37532 40357 35489 29027 34485 42598 30306 26451 47460 50104 61465 53726 39477 43895 31481 29896 33842 39120 33702 25094 51442 45594 52518 48564 41745 49585 32747 33379 35645 37034 35681 20972
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


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


Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
15860862959.055757149112659.138998096641597.80524475434351.05575714909
24686544374.91070397387700.9284981463641654.1607978798-2490.08929602618
35137847757.968661380713287.514987614041710.5163510053-3620.03133861929
44623541954.47170166988768.6005041553641746.9277941749-4280.52829833023
54720651335.17816921781293.4825934377541783.33923734444129.1781692178
64538245617.97515181373329.3778823546741816.6469658316235.975151813698
74122745501.9692566196-4897.9239509384741849.95469431884274.96925661964
83379535277.0634015509-9623.8548367232541936.79143517241482.06340155088
93129527233.7535425662-6667.3817185921442023.6281760259-4061.24645743379
104262543410.0818866608-239.48729394134342079.4054072805785.081886660824
113362533082.8131024032-7967.9957409382742135.1826385351-542.186897596832
122153818899.6313709003-17642.398045194641818.7666742943-2638.36862909968
135642158680.5102918512659.138998096641502.35071005342259.51029185000
145315257483.31719390717700.9284981463641119.75430794654331.3171939071
155353653047.327106546413287.514987614040737.1579058397-488.672893453637
165240855699.9720737448768.6005041553640347.42742210063291.97207374402
174145441656.82046820071293.4825934377539957.6969383616202.820468200669
183827133755.28883624283329.3778823546739457.3332814025-4515.71116375719
193530636552.954326495-4897.9239509384738956.96962444351246.95432649500
202641424045.7743335144-9623.8548367232538406.0805032088-2368.22566648555
213191732646.190336618-6667.3817185921437855.1913819741729.190336618005
223803038846.7775612977-239.48729394134337452.7097326436816.77756129772
232753425985.7676576252-7967.9957409382737050.2280833131-1548.23234237484
241838717486.7685055575-17642.398045194636929.6295396371-900.231494442545
255055651643.830005942312659.138998096636809.03099596111087.83000594228
264390143155.71926502977700.9284981463636945.3522368239-745.280734970285
274857246774.811534699313287.514987614037081.6734776867-1797.18846530069
284389941627.10944359488768.6005041553637402.2900522498-2271.89055640517
293753236047.61077974931293.4825934377537722.9066268129-1484.38922025066
304035739274.91110876413329.3778823546738109.7110088813-1082.08889123594
313548937379.4085599888-4897.9239509384738496.51539094961890.40855998883
322902728649.9373680031-9623.8548367232539027.9174687201-377.062631996851
333448536078.0621721016-6667.3817185921439559.31954649061593.06217210157
344259845318.3307580675-239.48729394134340117.15653587392720.33075806747
353030627905.0022156811-7967.9957409382740674.9935252572-2400.99778431891
362645129640.5227427886-17642.398045194640903.8753024063189.52274278855
374746041128.103922348512659.138998096641132.7570795549-6331.89607765145
385010451436.56850467117700.9284981463641070.50299718261332.56850467108
396146568634.236097575813287.514987614041008.24891481037169.23609757576
405372657746.27729378878768.6005041553640937.1222020564020.27729378868
413947736794.52191726061293.4825934377540865.9954893017-2682.47808273941
424389543670.17629606073329.3778823546740790.4458215847-224.823703939321
433148127145.0277970708-4897.9239509384740714.8961538677-4335.97220292918
442989628935.723364075-9623.8548367232540480.1314726482-960.276635924973
453384234106.0149271633-6667.3817185921440245.3667914288264.014927163335
463912038405.8940445726-239.48729394134340073.5932493687-714.105955427374
473370235470.1760336297-7967.9957409382739901.81970730861768.17603362965
482509427783.4331478126-17642.398045194640046.9648973822689.43314781261
495144250032.750914448112659.138998096640192.1100874553-1409.24908555191
504559443124.62604556247700.9284981463640362.4454562912-2469.37395443759
515251851215.704187258913287.514987614040532.7808251271-1302.29581274110
524856447779.57358739438768.6005041553640579.8259084503-784.426412605695
534174541569.64641478871293.4825934377540626.8709917736-175.353585211305
544958555178.69180119233329.3778823546740661.9303164535593.69180119233
553274729694.9343098060-4897.9239509384740696.9896411324-3052.06569019397
563337935640.1289825633-9623.8548367232540741.72585416002261.12898256329
573564537170.9196514047-6667.3817185921440786.46206718751525.91965140466
583703433479.7931962969-239.48729394134340827.6940976444-3554.20680370305
593568138461.0696128370-7967.9957409382740868.92612810132780.06961283696
602097218685.8380104027-17642.398045194640900.5600347919-2286.16198959728
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2009/Dec/04/t12599437160pa8kai2x2lffdz/1hj5k1259943649.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/04/t12599437160pa8kai2x2lffdz/1hj5k1259943649.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/04/t12599437160pa8kai2x2lffdz/2xmcu1259943649.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/04/t12599437160pa8kai2x2lffdz/2xmcu1259943649.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/04/t12599437160pa8kai2x2lffdz/3m3re1259943649.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/04/t12599437160pa8kai2x2lffdz/3m3re1259943649.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/04/t12599437160pa8kai2x2lffdz/4fdwu1259943649.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/04/t12599437160pa8kai2x2lffdz/4fdwu1259943649.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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