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Data X:
105.5227475 99.96697807 99.29276788 97.61994679 92.62666004 90.32801498 85.52083209 92.71339449 96.51595626 103.9418082 106.9973474 113.2595822 109.7190934 98.37704602 95.34417091 93.02005401 98.46060437 96.29026019 92.64756809 102.3220594 101.3264016 114.6738496 102.9524431 109.4774482 101.6334513 98.57578752 104.3007445 107.9441506 101.8381511 106.7241893 89.66204355 90.05567866 113.30134 112.3883223 95.92201437 95.67776984 100.917002 98.97327054 110.0791791 110.1929871 103.5780994 111.7920977 96.78877956 95.98442935 118.9305845 111.295244 102.4709069 97.82438647 98.25590457 95.1971819 99.19646064 104.468676 100.7123022 110.6990195 83.40207273 93.32671353 110.4355427 101.1594271 100.544762 95.47333016 102.4522505 97.88019225 108.7308777 103.6509173 109.6167435 110.1027949 86.96544074 97.61994679 114.0177893 112.2889515 103.5302866 98.02882615 108.7979443 104.5380327 112.8720892 109.2730085 110.7425924 109.3078289 92.55126085 102.8331586
Data Y:
101.6041349 96.48530635 101.0183779 100.9789536 103.1498368 106.0353826 86.54664403 102.2333505 110.6722524 115.6839132 99.88333999 101.8152182 102.531556 96.48530635 98.65371554 105.1602767 104.1803047 102.2941156 91.65431482 99.8290897 111.7027203 115.1916413 98.18078306 105.3693429 113.9697497 102.39257 106.5043947 112.1639929 101.9132753 108.1029249 99.22123452 106.8328059 127.6749728 122.9695384 106.5989812 127.4258224 103.6650707 102.6879332 113.8821414 112.3730591 106.0351469 123.6587192 101.6804834 113.7319891 132.3120783 119.2282714 113.030863 134.5340717 112.0118607 103.8693859 113.3146225 116.3453161 113.9697497 118.2437275 92.2218338 112.5821252 131.1785637 115.38855 113.3146225 121.4674369 114.3819369 110.6627391 123.529964 114.0455883 122.4195865 121.9849945 96.09988014 117.9133122 131.8998912 127.5968949 118.9898122 124.9170285 119.225136 111.8441918 122.6786856 117.0770476 120.3586507 124.3478999 106.7881542 121.1538377
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) 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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R Server
Big Analytics Cloud Computing Center
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