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Gedifferentieerde tijdreeks

*The author of this computation has been verified*

R Software Module: /rwasp_autocorrelation.wasp (opens new window with default values)

Title produced by software: (Partial) Autocorrelation Function

Date of computation: Sun, 26 Dec 2010 13:27:39 +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/26/t1293369960e7zbs53jywzriub.htm/, Retrieved Sun, 26 Dec 2010 14:26:00 +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/26/t1293369960e7zbs53jywzriub.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 «

16198.90 16554.20 19554.20 15903.80 18003.80 18329.60 16260.70 14851.90 18174.10 18406.60 18466.50 16016.50 17428.50 17167.20 19630.00 17183.60 18344.70 19301.40 18147.50 16192.90 18374.40 20515.20 18957.20 16471.50 18746.80 19009.50 19211.20 20547.70 19325.80 20605.50 20056.90 16141.40 20359.80 19711.60 15638.60 14384.50 13721.40 14134.30 15021.70 14212.60 13635.00 15446.90 14762.10 12521.00 16236.80 16065.00 16032.10 15794.30 15160.00 15692.10 18908.90 17424.50 17014.20 19790.40 17681.20 16006.90 19601.70

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	2 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Autocorrelation Function
Time lag k	ACF(k)	T-STAT	P-value
1	-0.355101	-2.3555	0.011511
2	0.049435	0.3279	0.372267
3	0.450301	2.987	0.002296
4	-0.260388	-1.7272	0.045571
5	0.153904	1.0209	0.156445
6	0.15383	1.0204	0.15656
7	-0.300179	-1.9912	0.026347
8	0.168034	1.1146	0.135535
9	-0.11603	-0.7697	0.222809
10	-0.135399	-0.8981	0.187001
11	0.06993	0.4639	0.322515
12	-0.161356	-1.0703	0.145158
13	-0.1427	-0.9466	0.174516
14	0.062991	0.4178	0.33905
15	-0.069133	-0.4586	0.324397
16	-0.065154	-0.4322	0.333861
17	0.004619	0.0306	0.487848
18	-0.026237	-0.174	0.431317
19	-0.021105	-0.14	0.444652
20	0.088792	0.589	0.279444
21	-0.03553	-0.2357	0.407387
22	-0.036937	-0.245	0.403794
23	0.165303	1.0965	0.139414
24	-0.186666	-1.2382	0.111104
25	0.086945	0.5767	0.28353
26	0.069816	0.4631	0.322785
27	-0.148108	-0.9824	0.165629
28	0.075477	0.5007	0.309553
29	0.045593	0.3024	0.381874
30	-0.080719	-0.5354	0.297524
31	0.023361	0.155	0.438782
32	0.045045	0.2988	0.383252
33	-0.06912	-0.4585	0.324428
34	-0.006268	-0.0416	0.483512
35	0.038763	0.2571	0.399141
36	-0.029914	-0.1984	0.421812
37	-0.020357	-0.135	0.446601
38	0.028913	0.1918	0.424395
39	-0.02749	-0.1823	0.428073
40	0.009576	0.0635	0.47482
41	0.004509	0.0299	0.488137
42	-0.002304	-0.0153	0.493937
43	0.001059	0.007	0.497214
44	NA	NA	NA
45	NA	NA	NA
46	NA	NA	NA
47	NA	NA	NA
48	NA	NA	NA
49	NA	NA	NA
50	NA	NA	NA
51	NA	NA	NA
52	NA	NA	NA
53	NA	NA	NA
54	NA	NA	NA
55	NA	NA	NA
56	NA	NA	NA
57	NA	NA	NA
58	NA	NA	NA
59	NA	NA	NA
60	NA	NA	NA

Partial Autocorrelation Function
Time lag k	PACF(k)	T-STAT	P-value
1	-0.355101	-2.3555	0.011511
2	-0.087724	-0.5819	0.281803
3	0.505368	3.3522	0.000828
4	0.104881	0.6957	0.245137
5	0.028559	0.1894	0.42531
6	-0.003108	-0.0206	0.491823
7	-0.259668	-1.7224	0.046006
8	-0.159452	-1.0577	0.147986
9	-0.203845	-1.3522	0.091618
10	-0.033448	-0.2219	0.412722
11	0.015733	0.1044	0.458679
12	0.038779	0.2572	0.3991
13	-0.148481	-0.9849	0.165027
14	-0.099109	-0.6574	0.257169
15	0.105288	0.6984	0.2443
16	0.09371	0.6216	0.268706
17	-0.023306	-0.1546	0.438924
18	-0.076474	-0.5073	0.307249
19	-0.09101	-0.6037	0.274574
20	0.018156	0.1204	0.452344
21	0.016709	0.1108	0.456126
22	-0.088346	-0.586	0.280428
23	0.089228	0.5919	0.278483
24	-0.173581	-1.1514	0.127892
25	-0.12459	-0.8264	0.206507
26	-0.082841	-0.5495	0.292719
27	0.09003	0.5972	0.276719
28	0.072144	0.4785	0.317316
29	0.081698	0.5419	0.295302
30	0.064273	0.4263	0.33597
31	-0.183405	-1.2166	0.115126
32	-0.018724	-0.1242	0.450861
33	-0.043425	-0.2881	0.38733
34	-0.082887	-0.5498	0.292615
35	-0.065095	-0.4318	0.334002
36	0.051767	0.3434	0.366473
37	-0.035537	-0.2357	0.40737
38	-0.041796	-0.2772	0.391445
39	0.04903	0.3252	0.373274
40	0.043989	0.2918	0.38591
41	-0.027129	-0.18	0.429007
42	-0.003354	-0.0222	0.491176
43	-0.055919	-0.3709	0.356236
44	NA	NA	NA
45	NA	NA	NA
46	NA	NA	NA
47	NA	NA	NA
48	NA	NA	NA
49	NA	NA	NA
50	NA	NA	NA
51	NA	NA	NA
52	NA	NA	NA
53	NA	NA	NA
54	NA	NA	NA
55	NA	NA	NA
56	NA	NA	NA
57	NA	NA	NA
58	NA	NA	NA
59	NA	NA	NA
60	NA	NA	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293369960e7zbs53jywzriub/1ygnw1293370056.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293369960e7zbs53jywzriub/1ygnw1293370056.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293369960e7zbs53jywzriub/2rpmz1293370056.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293369960e7zbs53jywzriub/2rpmz1293370056.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293369960e7zbs53jywzriub/3rpmz1293370056.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293369960e7zbs53jywzriub/3rpmz1293370056.ps (open in new window)

Parameters (Session):

par1 = 60 ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;

Parameters (R input):

par1 = 60 ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;

R code (references can be found in the software module):

if (par1 == 'Default') {

par1 = 10*log10(length(x))

} else {

par1 <- as.numeric(par1)

}

par2 <- as.numeric(par2)

par3 <- as.numeric(par3)

par4 <- as.numeric(par4)

par5 <- as.numeric(par5)

if (par6 == 'White Noise') par6 <- 'white' else par6 <- 'ma'

par7 <- as.numeric(par7)

if (par8 != '') par8 <- as.numeric(par8)

ox <- x

if (par8 == '') {

if (par2 == 0) {

x <- log(x)

} else {

x <- (x ^ par2 - 1) / par2

}

} else {

x <- log(x,base=par8)

}

if (par3 > 0) x <- diff(x,lag=1,difference=par3)

if (par4 > 0) x <- diff(x,lag=par5,difference=par4)

bitmap(file='picts.png')

op <- par(mfrow=c(2,1))

plot(ox,type='l',main='Original Time Series',xlab='time',ylab='value')

if (par8=='') {

mytitle <- paste('Working Time Series (lambda=',par2,', d=',par3,', D=',par4,')',sep='')

mysub <- paste('(lambda=',par2,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep='')

} else {

mytitle <- paste('Working Time Series (base=',par8,', d=',par3,', D=',par4,')',sep='')

mysub <- paste('(base=',par8,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep='')

}

plot(x,type='l', main=mytitle,xlab='time',ylab='value')

par(op)

dev.off()

bitmap(file='pic1.png')

racf <- acf(x, par1, main='Autocorrelation', xlab='time lag', ylab='ACF', ci.type=par6, ci=par7, sub=mysub)

dev.off()

bitmap(file='pic2.png')

rpacf <- pacf(x,par1,main='Partial Autocorrelation',xlab='lags',ylab='PACF',sub=mysub)

dev.off()

(myacf <- c(racf$acf))

(mypacf <- c(rpacf$acf))

lengthx <- length(x)

sqrtn <- sqrt(lengthx)

load(file='createtable')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'Autocorrelation Function',4,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'Time lag k',header=TRUE)

a<-table.element(a,hyperlink('http://www.xycoon.com/basics.htm','ACF(k)','click here for more information about the Autocorrelation Function'),header=TRUE)

a<-table.element(a,'T-STAT',header=TRUE)

a<-table.element(a,'P-value',header=TRUE)

a<-table.row.end(a)

for (i in 2:(par1+1)) {

a<-table.row.start(a)

a<-table.element(a,i-1,header=TRUE)

a<-table.element(a,round(myacf[i],6))

mytstat <- myacf[i]*sqrtn

a<-table.element(a,round(mytstat,4))

a<-table.element(a,round(1-pt(abs(mytstat),lengthx),6))

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,'Partial Autocorrelation Function',4,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'Time lag k',header=TRUE)

a<-table.element(a,hyperlink('http://www.xycoon.com/basics.htm','PACF(k)','click here for more information about the Partial Autocorrelation Function'),header=TRUE)

a<-table.element(a,'T-STAT',header=TRUE)

a<-table.element(a,'P-value',header=TRUE)

a<-table.row.end(a)

for (i in 1:par1) {

a<-table.row.start(a)

a<-table.element(a,i,header=TRUE)

a<-table.element(a,round(mypacf[i],6))

mytstat <- mypacf[i]*sqrtn

a<-table.element(a,round(mytstat,4))

a<-table.element(a,round(1-pt(abs(mytstat),lengthx),6))

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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