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Data X:
13.5 16.2 17.6 15.8 17.6 15.2 15.9 12.0 13.3 14.8 16.1 16.9 17.6 13.9 10.0 7.6 7.1 8.1 8.1 7.7 4.0 1.4 0.3 -1.0 -1.9 -1.5 -0.2 3.4 3.0 4.1 3.4 3.2 6.1 5.8 6.2 5.8 5.9 6.7 5.9 3.8 1.7 1.4 1.8 3.0 3.6 4.8 4.3 4.2 2.9 4.9 7.2 8.7 9.1 8.9 9.0 11.6 9.6 9.1 9.2 10.8 11.0 8.5 6.5 7.2 7.8 8.7 7.8 7.5 7.7 7.5 8.3 7.9 10.4 11.5 14.0 11.9 11.9 10.3 11.3 9.9 8.9 9.2 8.8 6.7 7.1 6.6 7.2 5.0 5.3 6.3
Data Y:
-12.7 -2.4 7.1 -3.9 9.5 5 -16.1 -10.8 7 13.6 8.1 -8.1 4.9 -0.8 4.3 4 1.5 5.4 -11.3 -16.4 -2 8.9 -7.2 -18 1.3 6.3 -6 2.8 2 5.1 -7.6 -18.6 5.8 20.3 0.7 -11.2 -5.7 -0.1 3.4 3.3 -1.2 4.2 -8.8 -25.3 8.5 14.5 -3.1 -10.4 -2.9 0.3 22.6 15.4 9 29.1 2.8 -3.8 27.7 28.9 26.5 19.8 13.2 14.1 34.1 30 21.8 32.1 5.3 3 17.1 26.3 38.1 19.5 38 35.5 78.6 62.2 76.9 104.9 32.2 42.5 64.3 74.9 75.4 43 58.7 55.4 76.6 63.3 78.9 82.7
Sample Range:
(leave blank to include all observations)
From:
To:
Box-Cox transformation parameter (X series)
1
-2.0
-1.9
-1.8
-1.7
-1.6
-1.5
-1.4
-1.3
-1.2
-1.1
-1.0
-0.9
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2.0
Degree (d) of non-seasonal differencing (X series)
0
1
2
Degree (D) of seasonal differencing (X series)
0
1
2
Seasonal Period
1
2
3
4
12
Box-Cox transformation parameter (Y series)
1
-2.0
-1.9
-1.8
-1.7
-1.6
-1.5
-1.4
-1.3
-1.2
-1.1
-1.0
-0.9
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2.0
Degree (d) of non-seasonal differencing (Y series)
0
1
2
Degree (D) of seasonal differencing (Y series)
0
1
2
Treatment of missing data
(?)
na.fail
na.pass
Chart options
Label y-axis:
Label x-axis:
R Code
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) x <- 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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Big Analytics Cloud Computing Center
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