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Data X:
101.8 103.4 104.9 105.1 105.6 104.5 105.5 105.1 106.9 106.6 106.6 106.5 109.7 109.5 109.2 109.1 109.0 109.0 109.0 109.0 109.0 109.0 109.0 109.0 109.0 109.0 109.0 109.0 109.0 109.0 109.0 109.0 109.0 109.0 109.0 109.2 113.3 112.3 112.3 116.3 118.3 119.4 119.4 119.4 120.1 121.7 123.7 123.7 128.5 127.1 122.6 119.8 122.7 123.4 123.8 121.8 121.2 121.2 121.2 121.2 129.6 131.0 131.0 129.8 129.8 134.9 131.2 127.1 130.5 130.5 131.7 131.7 131.7
Data Y:
100.0 100.0 100.0 100.1 100.0 100.0 99.8 100.0 99.9 99.2 98.7 98.7 98.9 99.2 99.8 100.5 100.1 100.5 98.4 98.6 99.0 99.1 98.9 98.5 96.9 96.8 97.0 97.0 96.9 97.1 97.2 97.9 98.9 99.2 99.5 99.3 99.9 100.0 100.3 100.5 100.7 100.9 100.8 100.9 101.0 100.3 100.1 99.8 99.9 99.9 100.2 99.7 100.4 100.9 101.3 101.4 101.3 100.9 100.9 100.9 101.1 101.1 101.3 101.8 102.9 103.2 103.3 104.5 105.0 104.9 104.9 105.4 106.0
Sample Range:
(leave blank to include all observations)
From:
To:
Box-Cox transformation parameter (X series)
Default
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)
1
0
1
2
Degree (D) of seasonal differencing (X series)
0
0
1
2
Seasonal Period
0
1
2
3
4
12
Box-Cox transformation parameter (Y series)
12
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',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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0 seconds
R Server
Big Analytics Cloud Computing Center
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