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Data X:
85.0 95.9 108.9 96.2 100.1 105.7 64.5 66.8 110.3 96.1 102.5 97.6 83.6 86.5 96.0 91.1 87.2 84.5 59.2 61.5 98.8 97.9 92.7 84.2 74.5 79.7 86.8 79.8 87.0 91.4 58.7 62.8 87.9 90.4 80.6 73.5 71.4 70.6 78.3 76.0 77.4 80.9 63.4 58.1 88.2 81.2 84.9 76.4 71.5 76.1 82.9 78.0 82.0 84.7 55.7 59.5 83.2 87.6 76.2 76.4 68.3 70.0 76.3 70.9 72.4 80.1 57.4 62.7 82.6 88.9 80.4 72.0 69.4 69.2 77.3 79.4 78.6 76.1 61.8 59.4 78.1
Data Y:
99.5 98.2 108.9 100.0 105.0 108.4 96.7 100.5 115.6 114.9 110.7 107.7 113.5 106.9 119.6 109.4 106.9 118.7 108.9 113.1 125.1 126.5 122.7 127.5 107.1 112.0 122.1 111.5 113.2 128.2 115.1 117.4 132.0 130.8 128.0 132.7 117.0 110.9 123.5 117.4 122.7 123.5 111.5 113.8 131.2 127.0 126.2 121.2 118.8 117.9 135.2 120.7 126.4 129.6 113.4 120.5 135.5 137.6 130.6 133.1 121.5 120.5 136.9 123.7 128.5 135.0 120.9 121.1 132.2 134.5 133.6 136.1 124.5 124.6 133.5 132.3 125.3 135.5 121.2 117.5 135.9
Sample Range:
(leave blank to include all observations)
From:
To:
Box-Cox transformation parameter (X series)
-1.0
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)
1
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)
1
0
1
2
Degree (D) of seasonal differencing (Y series)
1
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')
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Raw Input
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Raw Output
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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