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ws 8 Ad hoc forecasting link 2

*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: Wed, 02 Dec 2009 13:09:47 -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/02/t1259784665g90pizk28uukjjd.htm/, Retrieved Wed, 02 Dec 2009 21:11:24 +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/02/t1259784665g90pizk28uukjjd.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:

ws 8 Ad hoc forecasting link 2

Dataseries X:

» Textbox « » Textfile « » CSV «

8.2 8.0 7.5 6.8 6.5 6.6 7.6 8.0 8.1 7.7 7.5 7.6 7.8 7.8 7.8 7.5 7.5 7.1 7.5 7.5 7.6 7.7 7.7 7.9 8.1 8.2 8.2 8.2 7.9 7.3 6.9 6.6 6.7 6.9 7.0 7.1 7.2 7.1 6.9 7.0 6.8 6.4 6.7 6.6 6.4 6.3 6.2 6.5 6.8 6.8 6.4 6.1 5.8 6.1 7.2 7.3 6.9 6.1 5.8 6.2 7.1 7.7 7.9 7.7 7.4 7.5 8.0 8.1

Output produced by software:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Seasonal Decomposition by Loess - Parameters
Component	Window	Degree	Jump
Seasonal	681	0	69
Trend	19	1	2
Low-pass	13	1	2

Seasonal Decomposition by Loess - Time Series Components
t	Observed	Fitted	Seasonal	Trend	Remainder
1	8.2	8.76289232300616	0.351821216209758	7.28528646078408	0.562892323006166
2	8	8.27474967145866	0.406846050296754	7.31840427824459	0.274749671458659
3	7.5	7.40327354283408	0.245204361460822	7.3515220957051	-0.0967264571659188
4	6.8	6.21066064797297	0.00580965090081608	7.38352970112621	-0.589339352027029
5	6.5	5.81804797810215	-0.233585284649477	7.41553730654733	-0.681952021897851
6	6.6	6.15406501168457	-0.394824680884551	7.44075966919999	-0.445934988315434
7	7.6	7.6567487931802	0.0772691749671581	7.46598203185264	0.0567487931801995
8	8	8.42057939993176	0.0891585038214113	7.49026209624683	0.420579399931761
9	8.1	8.65963624824254	0.0258215911164464	7.51454216064101	0.559636248242539
10	7.7	8.02450330976678	-0.185373225080392	7.56086991531362	0.324503309766775
11	7.5	7.68937052428445	-0.296568194270666	7.60719766998622	0.189370524284445
12	7.6	7.66933747732517	-0.0915790245976931	7.62224154727252	0.0693374773251749
13	7.8	7.61089335923143	0.351821216209758	7.63728542455882	-0.189106640768573
14	7.8	7.57757495122438	0.406846050296754	7.61557899847887	-0.222425048775625
15	7.8	7.76092306614025	0.245204361460822	7.59387257239892	-0.0390769338597465
16	7.5	7.40888284585702	0.00580965090081608	7.58530750324216	-0.091117154142979
17	7.5	7.65684285056408	-0.233585284649477	7.5767424340854	0.156842850564076
18	7.1	6.99581843010677	-0.394824680884551	7.59900625077778	-0.104181569893234
19	7.5	7.30146075756267	0.0772691749671581	7.62127006747017	-0.198539242437326
20	7.5	7.25330633454978	0.0891585038214113	7.6575351616288	-0.246693665450215
21	7.6	7.48037815309611	0.0258215911164464	7.69380025578744	-0.119621846903885
22	7.7	7.85135044321368	-0.185373225080392	7.7340227818667	0.151350443213684
23	7.7	7.9223228863247	-0.296568194270666	7.77424530794598	0.222322886324691
24	7.9	8.10633820072276	-0.0915790245976931	7.78524082387493	0.206338200722763
25	8.1	8.05194244398636	0.351821216209758	7.79623633980388	-0.0480575560136423
26	8.2	8.24520832981737	0.406846050296754	7.74794561988587	0.0452083298173722
27	8.2	8.45514073857132	0.245204361460822	7.69965489996786	0.255140738571316
28	8.2	8.77583873246106	0.00580965090081608	7.61835161663813	0.575838732461056
29	7.9	8.49653695134108	-0.233585284649477	7.5370483333084	0.596536951341083
30	7.3	7.54443483151936	-0.394824680884551	7.45038984936519	0.24443483151936
31	6.9	6.35899945961085	0.0772691749671581	7.36373136542199	-0.541000540389146
32	6.6	5.84357709485442	0.0891585038214113	7.26726440132417	-0.756422905145584
33	6.7	6.2033809716572	0.0258215911164464	7.17079743722636	-0.496619028342802
34	6.9	6.90172365480175	-0.185373225080392	7.08364957027864	0.00172365480174985
35	7	7.30006649093974	-0.296568194270666	6.99650170333093	0.300066490939737
36	7.1	7.34175318309095	-0.0915790245976931	6.94982584150674	0.241753183090953
37	7.2	7.1450288041077	0.351821216209758	6.90314997968255	-0.0549711958923078
38	7.1	6.91707733474839	0.406846050296754	6.87607661495486	-0.182922665251612
39	6.9	6.70579238831201	0.245204361460822	6.84900325022716	-0.194207611687986
40	7	7.18987751411169	0.00580965090081608	6.8043128349875	0.189877514111689
41	6.8	7.07396286490165	-0.233585284649477	6.75962241974782	0.273962864901653
42	6.4	6.48202229082464	-0.394824680884551	6.71280239005992	0.0820222908246357
43	6.7	6.65674846466083	0.0772691749671581	6.66598236037201	-0.0432515353391656
44	6.6	6.4886838618706	0.0891585038214113	6.62215763430798	-0.111316138129394
45	6.4	6.1958455006396	0.0258215911164464	6.57833290824396	-0.204154499360403
46	6.3	6.26060306130305	-0.185373225080392	6.52477016377734	-0.0393969386969530
47	6.2	6.22536077495993	-0.296568194270666	6.47120741931073	0.0253607749599345
48	6.5	6.64404891132415	-0.0915790245976931	6.44753011327354	0.144048911324153
49	6.8	6.82432597655389	0.351821216209758	6.42385280723635	0.024325976553893
50	6.8	6.74875064045759	0.406846050296754	6.44440330924566	-0.0512493595424104
51	6.4	6.08984182728422	0.245204361460822	6.46495381125496	-0.310158172715783
52	6.1	5.71482628607599	0.00580965090081608	6.4793640630232	-0.385173713924013
53	5.8	5.33981096985805	-0.233585284649477	6.49377431479143	-0.460189030141954
54	6.1	6.09002592496776	-0.394824680884551	6.50479875591679	-0.0099740750322388
55	7.2	7.8069076279907	0.0772691749671581	6.51582319704215	0.606907627990693
56	7.3	7.93492590156	0.0891585038214113	6.57591559461858	0.634925901560006
57	6.9	7.13817041668854	0.0258215911164464	6.63600799219501	0.23817041668854
58	6.1	5.64676565466351	-0.185373225080392	6.73860757041688	-0.453234345336485
59	5.8	5.05536104563193	-0.296568194270666	6.84120714863874	-0.744638954368073
60	6.2	5.52390786580087	-0.0915790245976931	6.96767115879683	-0.676092134199133
61	7.1	6.75404361483533	0.351821216209758	7.09413516895491	-0.345956385164672
62	7.7	7.77392349071539	0.406846050296754	7.21923045898786	0.0739234907153863
63	7.9	8.21046988951837	0.245204361460822	7.3443257490208	0.310469889518373
64	7.7	7.91633628897512	0.00580965090081608	7.47785406012407	0.216336288975115
65	7.4	7.42220291342215	-0.233585284649477	7.61138237122733	0.0222029134221451
66	7.5	7.64195819165105	-0.394824680884551	7.7528664892335	0.141958191651046
67	8	8.02838021779316	0.0772691749671581	7.89435060723968	0.0283802177931634
68	8.1	8.07076867723918	0.0891585038214113	8.04007281893941	-0.0292313227608236

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/02/t1259784665g90pizk28uukjjd/1d1wb1259784584.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/02/t1259784665g90pizk28uukjjd/1d1wb1259784584.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/02/t1259784665g90pizk28uukjjd/2akev1259784584.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/02/t1259784665g90pizk28uukjjd/2akev1259784584.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/02/t1259784665g90pizk28uukjjd/30avm1259784584.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/02/t1259784665g90pizk28uukjjd/30avm1259784584.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/02/t1259784665g90pizk28uukjjd/4u9c41259784584.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/02/t1259784665g90pizk28uukjjd/4u9c41259784584.ps (open in new window)

Parameters (Session):

par1 = 0.01 ; par2 = 0.99 ; par3 = 0.005 ;

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')

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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