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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_cross.wasp
Title produced by softwareCross Correlation Function
Date of computationMon, 01 Dec 2008 12:58:58 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Dec/01/t1228161745dbps14d8yhnvvxy.htm/, Retrieved Sun, 05 May 2024 09:38:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27296, Retrieved Sun, 05 May 2024 09:38:04 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact230

Tree of Dependent Computations

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  [Standard Deviation-Mean Plot] [q5 airline data] [2008-11-28 16:40:33] [44a98561a4b3e6ab8cd5a857b48b0914]
F RMP     [(Partial) Autocorrelation Function] [q6 ACF] [2008-11-29 18:07:14] [44a98561a4b3e6ab8cd5a857b48b0914]
F RMPD        [Cross Correlation Function] [Non stationary ti...] [2008-12-01 19:58:58] [07b7cf1321bc38017c2c7efcf91ca696] [Current]
Feedback Forum
2008-12-03 19:03:54 [Kevin Truyts] [reply] 
Goede conclusie.
Tijdens het college werd er het volgende over verteld:
Zo kunnen we de Cross Correlation function omschrijven als volgt:
Op de x-as vinden we zowel positieve als negatieve getallen terug, terwijl op de y-as de correlatie te vinden is (tussen -1 en +1).
De cross correlatie functie gaat zoeken naar de correlaties tussen 2 tijdreeksen.
Bij de berekeningen zien we een tabel staan. De tweede kolom geeft de correlatie van Xt tot k weer.
Xt van k-perioden geleden gecorreleerd met Yt. Wanneer k negatief is, gaan we k-perioden terug in het verleden. Als k positief is dan geldt dit voor de voorspelling naar de toekomst toe, maar als k = 0 => gewone correlatie tussen Xt en Yt.
In deze tabel en op de grafiek is dan ook af te lezen hoe groot het vertragingseffect is.
2008-12-05 12:05:56 [Nathalie Koulouris] [reply] 
De student heeft deze vraag correct opgelost.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
118,3
127,3
112,3
114,9
108,2
105,4
122,1
113,5
110
125,3
114,3
115,6
127,1
123
122,2
126,4
112,7
105,8
120,9
116,3
115,7
127,9
108,3
121,1
128,6
123,1
127,7
126,6
118,4
110
129,6
115,8
125,9
128,4
114
125,6
128,5
136,6
133,1
124,6
123,5
117,2
135,5
124,8
127,8
133,1
125,7
128,4
131,9
146,3
140,6
129,5
132,4
125,9
126,9
135,8
129,5
130,2
133,8
123,3
Dataseries Y:
Download CSV
Histogram 
99,2
99,5
99,7
99,6
100,1
100,3
100,5
100,7
100,9
101,1
101,1
101,1
101,3
100,5
100,3
100
100,1
100,2
100,5
100
100,7
101,2
101,6
101,7
101,5
101,1
101,2
101,1
101,4
101,3
101,6
102
103,2
103,4
103,6
104,8
105,2
105,1
105,1
105,7
106,2
105,9
106,1
106,5
106,7
107,1
107,5
107,9
109,2
110,1
110,2
110,4
110,5
110,8
111,2
111
111,1
111,1
111,1
111,5

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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=27296&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=27296&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27296&T=0

As an alternative you can also use a QR Code:  
QR Code


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Cross Correlation Function
ParameterValue
Box-Cox transformation parameter (lambda) of X series1
Degree of non-seasonal differencing (d) of X series0
Degree of seasonal differencing (D) of X series0
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series1
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-140.310138537947843
-130.33013921180692
-120.365801931612483
-110.397735039881597
-100.464299584884025
-90.521011054722271
-80.525175574177947
-70.540510952678206
-60.539502009150011
-50.543851851906106
-40.576132779399568
-30.600684134507634
-20.614922367254257
-10.63693113440332
00.640663763077334
10.616409243401526
20.604535751875823
30.556381048553595
40.504950815549894
50.43807249354311
60.369671816184389
70.340614002898163
80.288314102526747
90.248178171384044
100.229255395107486
110.174137944266531
120.151214332224157
130.147026587702506
140.136973639105249

\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
-14 & 0.310138537947843 \tabularnewline
-13 & 0.33013921180692 \tabularnewline
-12 & 0.365801931612483 \tabularnewline
-11 & 0.397735039881597 \tabularnewline
-10 & 0.464299584884025 \tabularnewline
-9 & 0.521011054722271 \tabularnewline
-8 & 0.525175574177947 \tabularnewline
-7 & 0.540510952678206 \tabularnewline
-6 & 0.539502009150011 \tabularnewline
-5 & 0.543851851906106 \tabularnewline
-4 & 0.576132779399568 \tabularnewline
-3 & 0.600684134507634 \tabularnewline
-2 & 0.614922367254257 \tabularnewline
-1 & 0.63693113440332 \tabularnewline
0 & 0.640663763077334 \tabularnewline
1 & 0.616409243401526 \tabularnewline
2 & 0.604535751875823 \tabularnewline
3 & 0.556381048553595 \tabularnewline
4 & 0.504950815549894 \tabularnewline
5 & 0.43807249354311 \tabularnewline
6 & 0.369671816184389 \tabularnewline
7 & 0.340614002898163 \tabularnewline
8 & 0.288314102526747 \tabularnewline
9 & 0.248178171384044 \tabularnewline
10 & 0.229255395107486 \tabularnewline
11 & 0.174137944266531 \tabularnewline
12 & 0.151214332224157 \tabularnewline
13 & 0.147026587702506 \tabularnewline
14 & 0.136973639105249 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27296&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]-14[/C][C]0.310138537947843[/C][/ROW]
[ROW][C]-13[/C][C]0.33013921180692[/C][/ROW]
[ROW][C]-12[/C][C]0.365801931612483[/C][/ROW]
[ROW][C]-11[/C][C]0.397735039881597[/C][/ROW]
[ROW][C]-10[/C][C]0.464299584884025[/C][/ROW]
[ROW][C]-9[/C][C]0.521011054722271[/C][/ROW]
[ROW][C]-8[/C][C]0.525175574177947[/C][/ROW]
[ROW][C]-7[/C][C]0.540510952678206[/C][/ROW]
[ROW][C]-6[/C][C]0.539502009150011[/C][/ROW]
[ROW][C]-5[/C][C]0.543851851906106[/C][/ROW]
[ROW][C]-4[/C][C]0.576132779399568[/C][/ROW]
[ROW][C]-3[/C][C]0.600684134507634[/C][/ROW]
[ROW][C]-2[/C][C]0.614922367254257[/C][/ROW]
[ROW][C]-1[/C][C]0.63693113440332[/C][/ROW]
[ROW][C]0[/C][C]0.640663763077334[/C][/ROW]
[ROW][C]1[/C][C]0.616409243401526[/C][/ROW]
[ROW][C]2[/C][C]0.604535751875823[/C][/ROW]
[ROW][C]3[/C][C]0.556381048553595[/C][/ROW]
[ROW][C]4[/C][C]0.504950815549894[/C][/ROW]
[ROW][C]5[/C][C]0.43807249354311[/C][/ROW]
[ROW][C]6[/C][C]0.369671816184389[/C][/ROW]
[ROW][C]7[/C][C]0.340614002898163[/C][/ROW]
[ROW][C]8[/C][C]0.288314102526747[/C][/ROW]
[ROW][C]9[/C][C]0.248178171384044[/C][/ROW]
[ROW][C]10[/C][C]0.229255395107486[/C][/ROW]
[ROW][C]11[/C][C]0.174137944266531[/C][/ROW]
[ROW][C]12[/C][C]0.151214332224157[/C][/ROW]
[ROW][C]13[/C][C]0.147026587702506[/C][/ROW]
[ROW][C]14[/C][C]0.136973639105249[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27296&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27296&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Cross Correlation Function
ParameterValue
Box-Cox transformation parameter (lambda) of X series1
Degree of non-seasonal differencing (d) of X series0
Degree of seasonal differencing (D) of X series0
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series1
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-140.310138537947843
-130.33013921180692
-120.365801931612483
-110.397735039881597
-100.464299584884025
-90.521011054722271
-80.525175574177947
-70.540510952678206
-60.539502009150011
-50.543851851906106
-40.576132779399568
-30.600684134507634
-20.614922367254257
-10.63693113440332
00.640663763077334
10.616409243401526
20.604535751875823
30.556381048553595
40.504950815549894
50.43807249354311
60.369671816184389
70.340614002898163
80.288314102526747
90.248178171384044
100.229255395107486
110.174137944266531
120.151214332224157
130.147026587702506
140.136973639105249


Figures (Output of Computation)

Input Parameters & R Code

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')