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ws9 forcasting 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: Fri, 04 Dec 2009 13:37:34 -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/t1259959092u5j9mitohjtlctt.htm/, Retrieved Fri, 04 Dec 2009 21:38:18 +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/t1259959092u5j9mitohjtlctt.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:

Dataseries X:

» Textbox « » Textfile « » CSV «

2.05 2.11 2.09 2.05 2.08 2.06 2.06 2.08 2.07 2.06 2.07 2.06 2.09 2.07 2.09 2.28 2.33 2.35 2.52 2.63 2.58 2.70 2.81 2.97 3.04 3.28 3.33 3.50 3.56 3.57 3.69 3.82 3.79 3.96 4.06 4.05 4.03 3.94 4.02 3.88 4.02 4.03 4.09 3.99 4.01 4.01 4.19 4.30 4.27 3.82 3.15 2.49 1.81 1.26 1.06 0.84 0.78 0.70 0.36 0.35

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	1 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Seasonal Decomposition by Loess - Parameters
Component	Window	Degree	Jump
Seasonal	601	0	61
Trend	19	1	2
Low-pass	13	1	2

Seasonal Decomposition by Loess - Time Series Components
t	Observed	Fitted	Seasonal	Trend	Remainder
1	2.05	1.88230216820302	0.169367043985608	2.04833078781137	-0.167697831796979
2	2.11	2.0292980805392	0.136321978219099	2.0543799412417	-0.0807019194607996
3	2.09	2.07229393667369	0.047276968654283	2.06042909467203	-0.0177060633263122
4	2.05	2.06353807359849	-0.027541235391977	2.06400316179349	0.0135380735984914
5	2.08	2.17878223404962	-0.086359462964564	2.06757722891494	0.0987822340496223
6	2.06	2.22237466109754	-0.172827629120262	2.07045296802272	0.162374661097538
7	2.06	2.16996712996999	-0.123295837100501	2.07332870713051	0.109967129969994
8	2.08	2.18663791610629	-0.103655302582279	2.07701738647599	0.106637916106287
9	2.07	2.15730878327765	-0.0980148490991238	2.08070606582148	0.0873087832776474
10	2.06	2.03689176925960	-0.000994051684549588	2.08410228242495	-0.0231082307404038
11	2.07	1.9844746885837	0.0680268123878688	2.08749849902843	-0.085525311416299
12	2.06	1.81758602804181	0.191695489216516	2.11071848274168	-0.242413971958192
13	2.09	1.87669448955947	0.169367043985608	2.13393846645492	-0.213305510440529
14	2.07	1.82386232980348	0.136321978219099	2.17981569197742	-0.246137670196518
15	2.09	1.90703011384580	0.047276968654283	2.22569291749991	-0.182969886154197
16	2.28	2.29743114416309	-0.027541235391977	2.29011009122888	0.0174311441630932
17	2.33	2.39183219800671	-0.086359462964564	2.35452726495785	0.0618321980067105
18	2.35	2.44042931957099	-0.172827629120262	2.43239830954927	0.090429319570994
19	2.52	2.65302648295982	-0.123295837100501	2.51026935414068	0.133026482959818
20	2.63	2.76616124041895	-0.103655302582279	2.59749406216333	0.136161240418947
21	2.58	2.57329607891314	-0.0980148490991238	2.68471877018598	-0.00670392108685558
22	2.7	2.62223409308283	-0.000994051684549588	2.77875995860172	-0.0777659069171719
23	2.81	2.67917204059467	0.0680268123878688	2.87280114701746	-0.130827959405332
24	2.97	2.77486321802808	0.191695489216516	2.97344129275541	-0.195136781971924
25	3.04	2.83655151752104	0.169367043985608	3.07408143849335	-0.203448482478962
26	3.28	3.24210410213461	0.136321978219099	3.18157391964629	-0.0378958978653858
27	3.33	3.3236566305465	0.047276968654283	3.28906640079922	-0.00634336945350089
28	3.5	3.62979853927993	-0.027541235391977	3.39774269611205	0.129798539279931
29	3.56	3.69994047153969	-0.086359462964564	3.50641899142488	0.139940471539689
30	3.57	3.71472123040134	-0.172827629120262	3.59810639871893	0.144721230401335
31	3.69	3.81350203108752	-0.123295837100501	3.68979380601298	0.123502031087523
32	3.82	3.99253394486455	-0.103655302582279	3.75112135771772	0.172533944864555
33	3.79	3.86556593967665	-0.0980148490991238	3.81244890942247	0.0755659396766535
34	3.96	4.07119904804452	-0.000994051684549588	3.84979500364003	0.111199048044517
35	4.06	4.16483208975454	0.0680268123878688	3.88714109785760	0.104832089754536
36	4.05	3.99264417150128	0.191695489216516	3.91566033928221	-0.0573558284987241
37	4.03	3.94645337530757	0.169367043985608	3.94417958070682	-0.083546624692429
38	3.94	3.77725858888577	0.136321978219099	3.96641943289513	-0.162741411114235
39	4.02	4.00406374626227	0.047276968654283	3.98865928508345	-0.0159362537377317
40	3.88	3.78091568630651	-0.027541235391977	4.00662554908547	-0.0990843136934925
41	4.02	4.10176764987707	-0.086359462964564	4.02459181308749	0.0817676498770732
42	4.03	4.193663152456	-0.172827629120262	4.03916447666426	0.163663152456001
43	4.09	4.24955869685947	-0.123295837100501	4.05373714024103	0.159558696859470
44	3.99	4.05157877486577	-0.103655302582279	4.03207652771651	0.0615787748657688
45	4.01	4.10759893390713	-0.0980148490991238	4.01041591519199	0.0975989339071335
46	4.01	4.11991747836723	-0.000994051684549588	3.90107657331732	0.109917478367232
47	4.19	4.52023595616949	0.0680268123878688	3.79173723144264	0.330235956169487
48	4.3	4.82461383145911	0.191695489216516	3.58369067932438	0.524613831459107
49	4.27	4.99498882880828	0.169367043985608	3.37564412720611	0.724988828808282
50	3.82	4.40242220656346	0.136321978219099	3.10125581521744	0.58242220656346
51	3.15	3.42585552811695	0.047276968654283	2.82686750322877	0.275855528116947
52	2.49	2.50374077518465	-0.027541235391977	2.50380046020733	0.0137407751846479
53	1.81	1.52562604577867	-0.086359462964564	2.18073341718589	-0.284373954221325
54	1.26	0.837658168057849	-0.172827629120262	1.85516946106241	-0.422341831942151
55	1.06	0.713690332161563	-0.123295837100501	1.52960550493894	-0.346309667838437
56	0.84	0.58472405964089	-0.103655302582279	1.19893124294139	-0.255275940359110
57	0.78	0.789757868155284	-0.0980148490991238	0.86825698094384	0.00975786815528412
58	0.7	0.8627606176834	-0.000994051684549588	0.53823343400115	0.1627606176834
59	0.36	0.443763300553672	0.0680268123878688	0.208209887058459	0.0837633005536717
60	0.35	0.627144102358934	0.191695489216516	-0.118839591575450	0.277144102358934

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259959092u5j9mitohjtlctt/118nf1259959051.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259959092u5j9mitohjtlctt/118nf1259959051.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259959092u5j9mitohjtlctt/2ssjt1259959051.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259959092u5j9mitohjtlctt/2ssjt1259959051.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259959092u5j9mitohjtlctt/3e0wd1259959051.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259959092u5j9mitohjtlctt/3e0wd1259959051.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259959092u5j9mitohjtlctt/4vclr1259959052.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259959092u5j9mitohjtlctt/4vclr1259959052.ps (open in new window)

Parameters (Session):

par1 = 60 ; par2 = 1.7 ; par3 = 2 ; par4 = 0 ; par5 = 12 ; par6 = MA ; par7 = 0.95 ;

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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