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Author*Unverified author*
R Software Modulerwasp_cross.wasp
Title produced by softwareCross Correlation Function
Date of computationThu, 18 Jul 2019 00:42:41 +0200
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2019/Jul/18/t1563403556rnuzgzx1y5e2duy.htm/, Retrieved Wed, 12 Aug 2020 15:26:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=318849, Retrieved Wed, 12 Aug 2020 15:26:18 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact20
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Cross Correlation Function] [mql to won weekly] [2019-07-17 22:42:41] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
23
100
42
33
31
51
29
29
32
29
40
38
31
38
25
49
53
22
38
36
30
39
56
49
29
6
11
47
41
42
27
13
39
51
33
49
36
32
37
26
41
20
39
34
43
44
31
24
32
34
36
26
Dataseries Y:
0
2
1
3
4
2
2
1
3
3
2
2
9
1
1
3
6
3
0
2
0
5
3
2
12
5
2
2
0
0
3
0
0
1
5
1
3
5
9
5
1
0
1
3
1
0
0
4
3
0
1
7




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time0 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time0 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318849&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]0 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=318849&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318849&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 Outputview raw output of R engine
Computing time0 seconds
R ServerBig Analytics Cloud Computing Center







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)1
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])
-140.0523955755359781
-13-0.285810694764963
-12-0.26517457583629
-110.320206125233923
-10-0.0400482475245485
-90.0145910390137702
-8-0.0337101912002001
-7-0.028880149561139
-60.00980494383612601
-50.00145267469683638
-4-0.0766198416570045
-30.191496705624139
-20.192031387571378
-10.0973698958560172
0-0.11197558369026
1-0.252268151176834
2-0.131728704273796
30.0210788388362798
4-0.0272126254973756
5-0.0503455544932157
60.14657935293927
70.0201885160809078
80.0202674569467863
9-0.10096048451845
10-0.00295580644196628
110.195979244688663
120.079533329284273
13-0.148316865811957
14-0.110679976901821

\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) & 1 \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 & 0.0523955755359781 \tabularnewline
-13 & -0.285810694764963 \tabularnewline
-12 & -0.26517457583629 \tabularnewline
-11 & 0.320206125233923 \tabularnewline
-10 & -0.0400482475245485 \tabularnewline
-9 & 0.0145910390137702 \tabularnewline
-8 & -0.0337101912002001 \tabularnewline
-7 & -0.028880149561139 \tabularnewline
-6 & 0.00980494383612601 \tabularnewline
-5 & 0.00145267469683638 \tabularnewline
-4 & -0.0766198416570045 \tabularnewline
-3 & 0.191496705624139 \tabularnewline
-2 & 0.192031387571378 \tabularnewline
-1 & 0.0973698958560172 \tabularnewline
0 & -0.11197558369026 \tabularnewline
1 & -0.252268151176834 \tabularnewline
2 & -0.131728704273796 \tabularnewline
3 & 0.0210788388362798 \tabularnewline
4 & -0.0272126254973756 \tabularnewline
5 & -0.0503455544932157 \tabularnewline
6 & 0.14657935293927 \tabularnewline
7 & 0.0201885160809078 \tabularnewline
8 & 0.0202674569467863 \tabularnewline
9 & -0.10096048451845 \tabularnewline
10 & -0.00295580644196628 \tabularnewline
11 & 0.195979244688663 \tabularnewline
12 & 0.079533329284273 \tabularnewline
13 & -0.148316865811957 \tabularnewline
14 & -0.110679976901821 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318849&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]1[/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]0.0523955755359781[/C][/ROW]
[ROW][C]-13[/C][C]-0.285810694764963[/C][/ROW]
[ROW][C]-12[/C][C]-0.26517457583629[/C][/ROW]
[ROW][C]-11[/C][C]0.320206125233923[/C][/ROW]
[ROW][C]-10[/C][C]-0.0400482475245485[/C][/ROW]
[ROW][C]-9[/C][C]0.0145910390137702[/C][/ROW]
[ROW][C]-8[/C][C]-0.0337101912002001[/C][/ROW]
[ROW][C]-7[/C][C]-0.028880149561139[/C][/ROW]
[ROW][C]-6[/C][C]0.00980494383612601[/C][/ROW]
[ROW][C]-5[/C][C]0.00145267469683638[/C][/ROW]
[ROW][C]-4[/C][C]-0.0766198416570045[/C][/ROW]
[ROW][C]-3[/C][C]0.191496705624139[/C][/ROW]
[ROW][C]-2[/C][C]0.192031387571378[/C][/ROW]
[ROW][C]-1[/C][C]0.0973698958560172[/C][/ROW]
[ROW][C]0[/C][C]-0.11197558369026[/C][/ROW]
[ROW][C]1[/C][C]-0.252268151176834[/C][/ROW]
[ROW][C]2[/C][C]-0.131728704273796[/C][/ROW]
[ROW][C]3[/C][C]0.0210788388362798[/C][/ROW]
[ROW][C]4[/C][C]-0.0272126254973756[/C][/ROW]
[ROW][C]5[/C][C]-0.0503455544932157[/C][/ROW]
[ROW][C]6[/C][C]0.14657935293927[/C][/ROW]
[ROW][C]7[/C][C]0.0201885160809078[/C][/ROW]
[ROW][C]8[/C][C]0.0202674569467863[/C][/ROW]
[ROW][C]9[/C][C]-0.10096048451845[/C][/ROW]
[ROW][C]10[/C][C]-0.00295580644196628[/C][/ROW]
[ROW][C]11[/C][C]0.195979244688663[/C][/ROW]
[ROW][C]12[/C][C]0.079533329284273[/C][/ROW]
[ROW][C]13[/C][C]-0.148316865811957[/C][/ROW]
[ROW][C]14[/C][C]-0.110679976901821[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318849&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318849&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
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)1
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])
-140.0523955755359781
-13-0.285810694764963
-12-0.26517457583629
-110.320206125233923
-10-0.0400482475245485
-90.0145910390137702
-8-0.0337101912002001
-7-0.028880149561139
-60.00980494383612601
-50.00145267469683638
-4-0.0766198416570045
-30.191496705624139
-20.192031387571378
-10.0973698958560172
0-0.11197558369026
1-0.252268151176834
2-0.131728704273796
30.0210788388362798
4-0.0272126254973756
5-0.0503455544932157
60.14657935293927
70.0201885160809078
80.0202674569467863
9-0.10096048451845
10-0.00295580644196628
110.195979244688663
120.079533329284273
13-0.148316865811957
14-0.110679976901821



Parameters (Session):
par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 1 ; par5 = 1 ; par6 = 0 ; par7 = 0 ; par8 = na.fail ;
Parameters (R input):
par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 1 ; par5 = 1 ; par6 = 0 ; par7 = 0 ; par8 = na.fail ;
R code (references can be found in the software module):
par8 <- 'na.fail'
par7 <- '0'
par6 <- '0'
par5 <- '1'
par4 <- '1'
par3 <- '0'
par2 <- '0'
par1 <- '1'
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 (par8=='na.fail') par8 <- na.fail else par8 <- na.pass
ccf <- function (x, y, lag.max = NULL, type = c('correlation', 'covariance'), plot = TRUE, na.action = na.fail, ...) {
type <- match.arg(type)
if (is.matrix(x) || is.matrix(y))
stop('univariate time series only')
X <- na.action(ts.intersect(as.ts(x), as.ts(y)))
colnames(X) <- c(deparse(substitute(x))[1L], deparse(substitute(y))[1L])
acf.out <- acf(X, lag.max = lag.max, plot = FALSE, type = type, na.action=na.action)
lag <- c(rev(acf.out$lag[-1, 2, 1]), acf.out$lag[, 1, 2])
y <- c(rev(acf.out$acf[-1, 2, 1]), acf.out$acf[, 1, 2])
acf.out$acf <- array(y, dim = c(length(y), 1L, 1L))
acf.out$lag <- array(lag, dim = c(length(y), 1L, 1L))
acf.out$snames <- paste(acf.out$snames, collapse = ' & ')
if (plot) {
plot(acf.out, ...)
return(invisible(acf.out))
}
else return(acf.out)
}
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)
print(x)
print(y)
bitmap(file='test1.png')
(r <- ccf(x,y,na.action=par8,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')