Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
101.8 103.4 104.9 105.1 105.6 104.5 105.5 105.1 106.9 106.6 106.6 106.5 109.7 109.5 109.2 109.1 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109.2 113.3 112.3 112.3 116.3 118.3 119.4 119.4 119.4 120.1 121.7 123.7 123.7 128.5 127.1 122.6 119.8 122.7 123.4 123.8 121.8 121.2 121.2 121.2 121.2 129.6 131 131 129.8 129.8 134.9 131.2 127.1 130.5 130.5 131.7 131.7 131.7 131.7 128.7 125 124.5 123 122.8 123.1 124.8 126.9 131.7 136.8 143.7 150.1 152.7 152.6 150.5 154.9 158 158.1 160.6 160.6
Data Y:
100 100 100 100.1 100 100 99.8 100 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 99.1 98.9 98.5 96.9 96.8 97 97 96.9 97.1 97.2 97.9 98.9 99.2 99.5 99.3 99.9 100 100.3 100.5 100.7 100.9 100.8 100.9 101 100.3 100.1 99.8 99.9 99.9 100.2 99.7 100.4 100.9 101.3 101.4 101.3 100.9 100.9 100.9 101.1 101.1 101.3 101.8 102.9 103.2 103.3 104.5 105 104.9 104.9 105.4 106 105.7 105.9 106.2 106.4 106.9 107.3 107.9 109.2 110.2 110.2 110.5 110.6 110.8 111.3 111.1 111.2 111.2 111.1 111.5 112.1 111.4
Sample Range:
(leave blank to include all observations)
From:
To:
Box-Cox transformation parameter (X series)
1
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
0
1
2
Degree (D) of seasonal differencing (X series)
0
0
1
2
Seasonal Period
12
1
2
3
4
12
Box-Cox transformation parameter (Y series)
1
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
0
1
2
Degree (D) of seasonal differencing (Y series)
0
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) 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')
Compute
Summary of computational transaction
Raw Input
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation