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Data X:
67.8 66.9 71.5 75.9 71.9 70.7 73.5 76.1 82.5 87.1 83.2 86.1 85.9 77.4 74.4 69.9 73.8 69.2 69.7 71.0 71.2 75.8 73.0 66.4 58.6 55.5 52.6 54.9 54.6 51.2 50.9 49.6 53.4 52.0 47.5 42.1 44.5 43.2 51.4 59.4 60.3 61.4 68.8 73.6 81.8 79.6 85.8 88.1 89.1 95.0 96.2 84.2 96.9 103.1 99.3 103.5 112.4 111.1 113.7 92.0 93.0 98.4 92.6 94.6 99.5 97.6 91.3 93.6 93.1 78.4 70.2 69.3 71.1 73.5 85.9 91.5 91.8 88.3 91.3 94.0 99.3 96.7 88.0 96.7 106.8 114.3 105.7 90.1 91.6 97.7 100.8 104.6 95.9 102.7 104.0 107.9 113.8 113.8 123.1 125.1 137.6 134.0 140.3 152.1 150.6 167.3 153.2 142.0 154.4 158.5 180.9 181.3 172.4 192.0 199.3 215.4 214.3 201.5 190.5 196.0 215.7 209.4 214.1 237.8 239.0 237.8 251.5 248.8 215.4 201.2 203.1 214.2 188.9 203.0 213.3 228.5 228.2 240.9 258.8 248.5 269.2 289.6 323.4 317.2 322.8 340.9 368.2 388.5 441.2 474.3 483.9 417.9 365.9 263.0 199.4
Data Y:
621.0 604.0 584.0 574.0 555.0 545.0 599.0 620.0 608.0 590.0 579.0 580.0 579.0 572.0 560.0 551.0 537.0 541.0 588.0 607.0 599.0 578.0 563.0 566.0 561.0 554.0 540.0 526.0 512.0 505.0 554.0 584.0 569.0 540.0 522.0 526.0 527.0 516.0 503.0 489.0 479.0 475.0 524.0 552.0 532.0 511.0 492.0 492.0 493.0 481.0 462.0 457.0 442.0 439.0 488.0 521.0 501.0 485.0 464.0 460.0 467.0 460.0 448.0 443.0 436.0 431.0 484.0 510.0 513.0 503.0 471.0 471.0 476.0 475.0 470.0 461.0 455.0 456.0 517.0 525.0 523.0 519.0 509.0 512.0 519.0 517.0 510.0 509.0 501.0 507.0 569.0 580.0 578.0 565.0 547.0 555.0 562.0 561.0 555.0 544.0 537.0 543.0 594.0 611.0 613.0 611.0 594.0 595.0 591.0 589.0 584.0 573.0 567.0 569.0 621.0 629.0 628.0 612.0 595.0 597.0 593.0 590.0 580.0 574.0 573.0 573.0 620.0 626.0 620.0 588.0 566.0 557.0 561.0 549.0 532.0 526.0 511.0 499.0 555.0 565.0 542.0 527.0 510.0 514.0 517.0 508.0 493.0 490.0 469.0 478.0 528.0 534.0 518.0 506.0 502.0
Sample Range:
(leave blank to include all observations)
From:
To:
Box-Cox transformation parameter (X series)
-0.1
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)
2
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)
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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0 seconds
R Server
Big Analytics Cloud Computing Center
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