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*Unverified author*

R Software Module

rwasp_cross.wasp

Title produced by software

Cross Correlation Function

Date of computation

Tue, 02 Dec 2008 12:55:38 -0700

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Dec/02/t1228247807m60ccuywbwkx7ew.htm/, Retrieved Sun, 19 May 2024 12:37:48 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28301, Retrieved Sun, 19 May 2024 12:37:48 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

188

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

F     [Univariate Data Series] [Airline data] [2007-10-18 09:58:47] [42daae401fd3def69a25014f2252b4c2]
F RMPD    [Cross Correlation Function] [] [2008-12-02 19:55:38] [c00776cbed2786c9c4960950021bd861] [Current]
F   P       [Cross Correlation Function] [Q9] [2008-12-03 12:45:14] [74be16979710d4c4e7c6647856088456] 

Feedback Forum

2008-12-03 19:05:53 [407693b66d7f2e0b350979005057872d] [reply] 
Dit is goed geantwoord omdat we X gebruiken om Y te voorspellen.
2008-12-04 16:08:31 [c97d2ae59c98cf77a04815c1edffab5a] [reply] 
wat de student zegt is juist. we gaan inderdaad onderzoeken of x(t) een leading indicator is (voorloper)/ een voorspellende kracht heeft op het verloop van y(t). 
hierbij gaan we k verschillende waarden laten aannemen, die overeenstemmen met verschuivingen in de tijd. uit de crosscorrelatie functie kan je afleiden hoeveel periodes je x(t) moet vertragen(k<0) of versnellen(k>0) om y(t) te voorspellen. je kan ook nagaan of x(t) een positief of negatief effect zal hebben op y(t) bij deze tijdsreeks zal dit een positief effect zijn. 
de rest heeft de student allemaal vermeld.
2008-12-07 18:19:45 [Sandra Hofmans] [reply] 
Je hebt goed begrepen wat een cross correlation functie inhoudt. We krijgen hier de grootste coefficient bij k=0. Dit betekent dat we Yt het beste kunnen voorspellen door middel van Xt, we hoeven dus niet naar het verleden of de toekomst te kijken. 
2008-12-08 13:06:29 [Dave Bellekens] [reply] 
Je geeft een correcte uitleg over het gebruik en interpretatie van de cross correlation functie.  
We zien hier op de grafiek dat de lag(k)=0 de hoogste correlatie coëfficiënt heeft, wat er op wijst dat Y(t)het best kunnen voorspellen adhv de huidige X waarden.

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Dataseries Y:

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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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 2 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28301&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28301&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28301&T=0

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

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

Cross Correlation Function
Parameter	Value
Box-Cox transformation parameter (lambda) of X series	1
Degree of non-seasonal differencing (d) of X series	0
Degree of seasonal differencing (D) of X series	0
Seasonal Period (s)	12
Box-Cox transformation parameter (lambda) of Y series	1
Degree of non-seasonal differencing (d) of Y series	0
Degree of seasonal differencing (D) of Y series	0
k	rho(Y[t],X[t+k])
-19	0.281875842657375
-18	0.305796412433953
-17	0.306281277843243
-16	0.304316983824934
-15	0.329351365270924
-14	0.402771228992521
-13	0.520993232023117
-12	0.621889078493107
-11	0.613621251493282
-10	0.550764316339467
-9	0.503517105625111
-8	0.491553073954822
-7	0.512570596928469
-6	0.525488040819997
-5	0.513450987097768
-4	0.494282726872846
-3	0.503513423932723
-2	0.561917418335251
-1	0.667754902299167
0	0.751857447677508
1	0.703708008466709
2	0.604126682658009
3	0.523037534948574
4	0.478622937677534
5	0.468941273322929
6	0.454815983111529
7	0.40942624078387
8	0.353727803348371
9	0.330344322280269
10	0.352608651235671
11	0.419089202846041
12	0.460382275559815
13	0.390298452408503
14	0.277305037425343
15	0.181278350390099
16	0.122774869591836
17	0.0999769349706572
18	0.0763971583982997
19	0.0249065424780193

\begin{tabular}{lllllllll}
\hline
Cross Correlation Function \tabularnewline
Parameter & Value \tabularnewline
Box-Cox transformation parameter (lambda) of X series & 1 \tabularnewline
Degree of non-seasonal differencing (d) of X series & 0 \tabularnewline
Degree of seasonal differencing (D) of X series & 0 \tabularnewline
Seasonal Period (s) & 12 \tabularnewline
Box-Cox transformation parameter (lambda) of Y series & 1 \tabularnewline
Degree of non-seasonal differencing (d) of Y series & 0 \tabularnewline
Degree of seasonal differencing (D) of Y series & 0 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-19 & 0.281875842657375 \tabularnewline
-18 & 0.305796412433953 \tabularnewline
-17 & 0.306281277843243 \tabularnewline
-16 & 0.304316983824934 \tabularnewline
-15 & 0.329351365270924 \tabularnewline
-14 & 0.402771228992521 \tabularnewline
-13 & 0.520993232023117 \tabularnewline
-12 & 0.621889078493107 \tabularnewline
-11 & 0.613621251493282 \tabularnewline
-10 & 0.550764316339467 \tabularnewline
-9 & 0.503517105625111 \tabularnewline
-8 & 0.491553073954822 \tabularnewline
-7 & 0.512570596928469 \tabularnewline
-6 & 0.525488040819997 \tabularnewline
-5 & 0.513450987097768 \tabularnewline
-4 & 0.494282726872846 \tabularnewline
-3 & 0.503513423932723 \tabularnewline
-2 & 0.561917418335251 \tabularnewline
-1 & 0.667754902299167 \tabularnewline
0 & 0.751857447677508 \tabularnewline
1 & 0.703708008466709 \tabularnewline
2 & 0.604126682658009 \tabularnewline
3 & 0.523037534948574 \tabularnewline
4 & 0.478622937677534 \tabularnewline
5 & 0.468941273322929 \tabularnewline
6 & 0.454815983111529 \tabularnewline
7 & 0.40942624078387 \tabularnewline
8 & 0.353727803348371 \tabularnewline
9 & 0.330344322280269 \tabularnewline
10 & 0.352608651235671 \tabularnewline
11 & 0.419089202846041 \tabularnewline
12 & 0.460382275559815 \tabularnewline
13 & 0.390298452408503 \tabularnewline
14 & 0.277305037425343 \tabularnewline
15 & 0.181278350390099 \tabularnewline
16 & 0.122774869591836 \tabularnewline
17 & 0.0999769349706572 \tabularnewline
18 & 0.0763971583982997 \tabularnewline
19 & 0.0249065424780193 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28301&T=1

[TABLE]
[ROW][C]Cross Correlation Function[/C][/ROW]
[ROW][C]Parameter[/C][C]Value[/C][/ROW]
[ROW][C]Box-Cox transformation parameter (lambda) of X series[/C][C]1[/C][/ROW]
[ROW][C]Degree of non-seasonal differencing (d) of X series[/C][C]0[/C][/ROW]
[ROW][C]Degree of seasonal differencing (D) of X series[/C][C]0[/C][/ROW]
[ROW][C]Seasonal Period (s)[/C][C]12[/C][/ROW]
[ROW][C]Box-Cox transformation parameter (lambda) of Y series[/C][C]1[/C][/ROW]
[ROW][C]Degree of non-seasonal differencing (d) of Y series[/C][C]0[/C][/ROW]
[ROW][C]Degree of seasonal differencing (D) of Y series[/C][C]0[/C][/ROW]
[ROW][C]k[/C][C]rho(Y[t],X[t+k])[/C][/ROW]
[ROW][C]-19[/C][C]0.281875842657375[/C][/ROW]
[ROW][C]-18[/C][C]0.305796412433953[/C][/ROW]
[ROW][C]-17[/C][C]0.306281277843243[/C][/ROW]
[ROW][C]-16[/C][C]0.304316983824934[/C][/ROW]
[ROW][C]-15[/C][C]0.329351365270924[/C][/ROW]
[ROW][C]-14[/C][C]0.402771228992521[/C][/ROW]
[ROW][C]-13[/C][C]0.520993232023117[/C][/ROW]
[ROW][C]-12[/C][C]0.621889078493107[/C][/ROW]
[ROW][C]-11[/C][C]0.613621251493282[/C][/ROW]
[ROW][C]-10[/C][C]0.550764316339467[/C][/ROW]
[ROW][C]-9[/C][C]0.503517105625111[/C][/ROW]
[ROW][C]-8[/C][C]0.491553073954822[/C][/ROW]
[ROW][C]-7[/C][C]0.512570596928469[/C][/ROW]
[ROW][C]-6[/C][C]0.525488040819997[/C][/ROW]
[ROW][C]-5[/C][C]0.513450987097768[/C][/ROW]
[ROW][C]-4[/C][C]0.494282726872846[/C][/ROW]
[ROW][C]-3[/C][C]0.503513423932723[/C][/ROW]
[ROW][C]-2[/C][C]0.561917418335251[/C][/ROW]
[ROW][C]-1[/C][C]0.667754902299167[/C][/ROW]
[ROW][C]0[/C][C]0.751857447677508[/C][/ROW]
[ROW][C]1[/C][C]0.703708008466709[/C][/ROW]
[ROW][C]2[/C][C]0.604126682658009[/C][/ROW]
[ROW][C]3[/C][C]0.523037534948574[/C][/ROW]
[ROW][C]4[/C][C]0.478622937677534[/C][/ROW]
[ROW][C]5[/C][C]0.468941273322929[/C][/ROW]
[ROW][C]6[/C][C]0.454815983111529[/C][/ROW]
[ROW][C]7[/C][C]0.40942624078387[/C][/ROW]
[ROW][C]8[/C][C]0.353727803348371[/C][/ROW]
[ROW][C]9[/C][C]0.330344322280269[/C][/ROW]
[ROW][C]10[/C][C]0.352608651235671[/C][/ROW]
[ROW][C]11[/C][C]0.419089202846041[/C][/ROW]
[ROW][C]12[/C][C]0.460382275559815[/C][/ROW]
[ROW][C]13[/C][C]0.390298452408503[/C][/ROW]
[ROW][C]14[/C][C]0.277305037425343[/C][/ROW]
[ROW][C]15[/C][C]0.181278350390099[/C][/ROW]
[ROW][C]16[/C][C]0.122774869591836[/C][/ROW]
[ROW][C]17[/C][C]0.0999769349706572[/C][/ROW]
[ROW][C]18[/C][C]0.0763971583982997[/C][/ROW]
[ROW][C]19[/C][C]0.0249065424780193[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28301&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28301&T=1

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Cross Correlation Function
Parameter	Value
Box-Cox transformation parameter (lambda) of X series	1
Degree of non-seasonal differencing (d) of X series	0
Degree of seasonal differencing (D) of X series	0
Seasonal Period (s)	12
Box-Cox transformation parameter (lambda) of Y series	1
Degree of non-seasonal differencing (d) of Y series	0
Degree of seasonal differencing (D) of Y series	0
k	rho(Y[t],X[t+k])
-19	0.281875842657375
-18	0.305796412433953
-17	0.306281277843243
-16	0.304316983824934
-15	0.329351365270924
-14	0.402771228992521
-13	0.520993232023117
-12	0.621889078493107
-11	0.613621251493282
-10	0.550764316339467
-9	0.503517105625111
-8	0.491553073954822
-7	0.512570596928469
-6	0.525488040819997
-5	0.513450987097768
-4	0.494282726872846
-3	0.503513423932723
-2	0.561917418335251
-1	0.667754902299167
0	0.751857447677508
1	0.703708008466709
2	0.604126682658009
3	0.523037534948574
4	0.478622937677534
5	0.468941273322929
6	0.454815983111529
7	0.40942624078387
8	0.353727803348371
9	0.330344322280269
10	0.352608651235671
11	0.419089202846041
12	0.460382275559815
13	0.390298452408503
14	0.277305037425343
15	0.181278350390099
16	0.122774869591836
17	0.0999769349706572
18	0.0763971583982997
19	0.0249065424780193

Figure 1

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 12 ; par5 = 1 ; par6 = 0 ; par7 = 0 ;

Parameters (R input):

par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 12 ; par5 = 1 ; par6 = 0 ; par7 = 0 ;

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

par1 <- as.numeric(par1)
par2 <- as.numeric(par2)
par3 <- as.numeric(par3)
par4 <- as.numeric(par4)
par5 <- as.numeric(par5)
par6 <- as.numeric(par6)
par7 <- as.numeric(par7)
if (par1 == 0) {
x <- log(x)
} else {
x <- (x ^ par1 - 1) / par1
}
if (par5 == 0) {
y <- log(y)
} else {
y <- (y ^ par5 - 1) / par5
}
if (par2 > 0) x <- diff(x,lag=1,difference=par2)
if (par6 > 0) y <- diff(y,lag=1,difference=par6)
if (par3 > 0) x <- diff(x,lag=par4,difference=par3)
if (par7 > 0) y <- diff(y,lag=par4,difference=par7)
x
y
bitmap(file='test1.png')
(r <- ccf(x,y,main='Cross Correlation Function',ylab='CCF',xlab='Lag (k)'))
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Cross Correlation Function',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'Value',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Box-Cox transformation parameter (lambda) of X series',header=TRUE)
a<-table.element(a,par1)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Degree of non-seasonal differencing (d) of X series',header=TRUE)
a<-table.element(a,par2)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Degree of seasonal differencing (D) of X series',header=TRUE)
a<-table.element(a,par3)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Seasonal Period (s)',header=TRUE)
a<-table.element(a,par4)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Box-Cox transformation parameter (lambda) of Y series',header=TRUE)
a<-table.element(a,par5)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Degree of non-seasonal differencing (d) of Y series',header=TRUE)
a<-table.element(a,par6)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Degree of seasonal differencing (D) of Y series',header=TRUE)
a<-table.element(a,par7)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'k',header=TRUE)
a<-table.element(a,'rho(Y[t],X[t+k])',header=TRUE)
a<-table.row.end(a)
mylength <- length(r$acf)
myhalf <- floor((mylength-1)/2)
for (i in 1:mylength) {
a<-table.row.start(a)
a<-table.element(a,i-myhalf-1,header=TRUE)
a<-table.element(a,r$acf[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')

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Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code