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LOESS: Faillissementen

*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: Sat, 11 Dec 2010 12:57:48 +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/11/t1292072192q9g5xjs8zefsynd.htm/, Retrieved Sat, 11 Dec 2010 13:56:33 +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/11/t1292072192q9g5xjs8zefsynd.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 «

46 62 66 59 58 61 41 27 58 70 49 59 44 36 72 45 56 54 53 35 61 52 47 51 52 63 74 45 51 64 36 30 55 64 39 40 63 45 59 55 40 64 27 28 45 57 45 69 60 56 58 50 51 53 37 22 55 70 62 58 39 49 58 47 42 62 39 40 72 70 54 65

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	'RServer@AstonUniversity' @ vre.aston.ac.uk

Seasonal Decomposition by Loess - Parameters
Component	Window	Degree	Jump
Seasonal	721	0	73
Trend	19	1	2
Low-pass	13	1	2

Seasonal Decomposition by Loess - Time Series Components
t	Observed	Fitted	Seasonal	Trend	Remainder
1	46	36.4219352653798	-1.06327449224725	56.6413392268674	-9.5780647346202
2	62	67.6930864217614	0.0994992650936779	56.2074143131449	5.69308642176144
3	66	63.4642247719535	12.7622858286241	55.7734893994223	-2.53577522804648
4	59	64.3254419639629	-1.64060804315987	55.315166079197	5.32544196396288
5	58	63.3533214485102	-2.21016420748182	54.8568427589717	5.35332144851017
6	61	59.8570228399605	7.74803404288557	54.394943117154	-1.14297716003954
7	41	41.1940625712891	-13.1271060466254	53.9330434753363	0.194062571289145
8	27	22.2720342413456	-21.6800365776476	53.408002336302	-4.72796575865441
9	58	57.5166778481113	5.60036095462091	52.8829611972678	-0.483322151888721
10	70	76.0689190070463	11.7094427070519	52.2216382859018	6.06891900704633
11	49	49.2878276758583	-2.84814305039416	51.5603153745358	0.287827675858345
12	59	61.8693205390778	4.6497123388434	51.4809671220788	2.8693205390778
13	44	37.6616556226255	-1.06327449224725	51.4016188696218	-6.33834437737453
14	36	20.2923432920945	0.0994992650936779	51.6081574428118	-15.7076567079055
15	72	79.423018155374	12.7622858286241	51.8146960160019	7.423018155374
16	45	39.880002307446	-1.64060804315987	51.7606057357139	-5.11999769255398
17	56	62.503648752056	-2.21016420748182	51.7065154554259	6.50364875205597
18	54	48.4577991365265	7.74803404288557	51.794166820588	-5.54220086347355
19	53	67.2452878608753	-13.1271060466254	51.8818181857501	14.2452878608753
20	35	39.3126183512332	-21.6800365776476	52.3674182264145	4.31261835123316
21	61	63.5466207783002	5.60036095462091	52.8530182670789	2.54662077830022
22	52	39.2521682773744	11.7094427070519	53.0383890155738	-12.7478317226256
23	47	43.6243832863255	-2.84814305039416	53.2237597640686	-3.37561671367448
24	51	44.2774550111896	4.6497123388434	53.072832649967	-6.72254498881042
25	52	52.1413689563818	-1.06327449224725	52.9219055358654	0.141368956381839
26	63	73.1432092674779	0.0994992650936779	52.7572914674285	10.1432092674779
27	74	82.6450367723843	12.7622858286241	52.5926773989915	8.64503677238433
28	45	39.1806383240464	-1.64060804315987	52.4599697191135	-5.8193616759536
29	51	51.8829021682464	-2.21016420748182	52.3272620392354	0.882902168246403
30	64	68.3917069773624	7.74803404288557	51.860258979752	4.39170697736238
31	36	33.7338501263568	-13.1271060466254	51.3932559202687	-2.26614987364324
32	30	30.9447079403865	-21.6800365776476	50.7353286372611	0.944707940386536
33	55	54.3222376911255	5.60036095462091	50.0774013542535	-0.677762308874456
34	64	66.6265755325198	11.7094427070519	49.6639817604283	2.62657553251983
35	39	31.5975808837911	-2.84814305039416	49.2505621666031	-7.40241911620893
36	40	26.4005892996235	4.6497123388434	48.9496983615331	-13.5994107003765
37	63	78.414439935784	-1.06327449224725	48.6488345564632	15.4144399357841
38	45	41.5675097874044	0.0994992650936779	48.3329909475019	-3.43249021259559
39	59	57.2205668328352	12.7622858286241	48.0171473385406	-1.77943316716477
40	55	63.6981659156896	-1.64060804315987	47.9424421274702	8.69816591568963
41	40	34.3424272910819	-2.21016420748182	47.8677369163999	-5.65757270891805
42	64	71.9010884776593	7.74803404288557	48.3508774794552	7.90108847765926
43	27	18.293088004115	-13.1271060466254	48.8340180425105	-8.70691199588502
44	28	28.3713481005724	-21.6800365776476	49.3086884770753	0.371348100572391
45	45	34.616280133739	5.60036095462091	49.78335891164	-10.383719866261
46	57	52.1660662920335	11.7094427070519	50.1244910009147	-4.83393370796654
47	45	42.3825199602048	-2.84814305039416	50.4656230901893	-2.61748003979518
48	69	82.652666042068	4.6497123388434	50.6976216190886	13.652666042068
49	60	70.1336543442595	-1.06327449224725	50.9296201479878	10.1336543442595
50	56	60.728421006318	0.0994992650936779	51.1720797285883	4.72842100631803
51	58	51.8231748621871	12.7622858286241	51.4145393091888	-6.17682513781293
52	50	49.9848409745742	-1.64060804315987	51.6557670685856	-0.0151590254257599
53	51	52.3131693794993	-2.21016420748182	51.8969948279825	1.31316937949935
54	53	46.5549140799125	7.74803404288557	51.6970518772019	-6.44508592008751
55	37	35.629997120204	-13.1271060466254	51.4971089264214	-1.37000287979597
56	22	14.5811344327679	-21.6800365776476	51.0989021448797	-7.41886556723209
57	55	53.698943682041	5.60036095462091	50.700695363338	-1.30105631795897
58	70	77.8184527562157	11.7094427070519	50.4721045367324	7.81845275621572
59	62	76.6046293402674	-2.84814305039416	50.2435137101268	14.6046293402674
60	58	61.0097818229296	4.6497123388434	50.340505838227	3.0097818229296
61	39	28.6257765259201	-1.06327449224725	50.4374979663272	-10.3742234740799
62	49	46.9813591915328	0.0994992650936779	50.9191415433735	-2.0186408084672
63	58	51.836929050956	12.7622858286241	51.4007851204198	-6.16307094904398
64	47	43.3006707792362	-1.64060804315987	52.3399372639237	-3.69932922076384
65	42	32.9310748000542	-2.21016420748182	53.2790894074276	-9.06892519994577
66	62	62.0819742703485	7.74803404288557	54.1699916867659	0.0819742703484891
67	39	36.0662120805211	-13.1271060466254	55.0608939661043	-2.93378791947886
68	40	45.6010195404533	-21.6800365776476	56.0790170371944	5.60101954045327
69	72	81.3024989370946	5.60036095462091	57.0971401082844	9.30249893709464
70	70	70.0437115666012	11.7094427070519	58.246845726347	0.0437115666012033
71	54	51.4515917059847	-2.84814305039416	59.3965513444094	-2.54840829401527
72	65	64.7433691464512	4.6497123388434	60.6069185147054	-0.25663085354882

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292072192q9g5xjs8zefsynd/1cj2n1292072263.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292072192q9g5xjs8zefsynd/1cj2n1292072263.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292072192q9g5xjs8zefsynd/2cj2n1292072263.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292072192q9g5xjs8zefsynd/2cj2n1292072263.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292072192q9g5xjs8zefsynd/3cj2n1292072263.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292072192q9g5xjs8zefsynd/3cj2n1292072263.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292072192q9g5xjs8zefsynd/4naj81292072263.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292072192q9g5xjs8zefsynd/4naj81292072263.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')

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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