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Data X:
609.95 609.62 608.17 459.88 671.17 873.34 894.90 884.64 771.35 755.46 399.05 806.63 801.54 894.51 770.97 722.85 696.07 811.00 834.23 849.46 911.93 984.20 946.65 969.18 899.54 988.84 880.13 836.42 876.78 868.84 863.87 885.55 792.98 785.66 729.56 672.49 572.95 509.42 530.77 497.14 392.33 393.13 389.25 372.69 356.30 387.31 442.63 445.02 531.54 581.40 686.42 746.32 792.16 686.87 428.67 363.76 435.34 471.05 580.61 665.05 724.65 544.68 533.68 419.25 431.69 380.29 390.91 451.33 518.11 545.90 493.21 576.50 679.14 739.60 710.26 719.43 786.23 832.74 833.73 821.45 764.06 807.42 828.93 804.86 766.85 758.54 711.30 518.63 604.25 538.65 515.42 514.74 560.44 564.83 608.19 625.44 590.04 461.97 619.76 781.35 669.21 794.68 765.52 755.92 703.50 504.19 480.18 422.67 635.18 688.50 804.74 802.68 815.08 724.27 698.89 707.05 735.06 822.93 782.90 736.33 692.23 730.05 795.33 745.38 703.52 695.08 729.65 741.11 778.90 953.28 1029.75 784.60 890.59 919.01 883.12 781.78 670.40 686.87 649.08 565.85 550.17 570.16 585.78 578.11 571.58 531.69 491.41 452.50 438.18 452.63 468.55 494.38 598.70 743.92 851.67 816.09 759.53 763.00 680.63 872.07 993.43 948.72 803.98 835.81 848.19 758.31 632.01 626.40 696.51 789.61 919.14 1011.02 1075.88 1207.03 1279.62 1240.37 1232.59 1269.13 1132.67 1116.30 1201.30 1302.47 1314.46 1242.01 1187.80 1111.50 1092.00 997.42 965.39 949.20 933.82 888.22 881.00 838.01 746.90 729.34 724.00 698.30 721.50 750.70 811.52 835.11 860.21 899.66 938.51 993.14 1007.93 1065.33 1117.15 1033.19 829.43 802.25 809.47 733.88 842.82 919.00 963.85 960.71 941.62 925.86 1094.77 1282.67 1362.51 1305.91 1303.04 1284.83 1248.47 1249.77 1290.07 1406.03 1446.90 1494.10 1494.48 1427.50 1431.89 1345.15 1283.16 1382.74 1309.63 1265.04 1123.16 1012.05 939.90 988.92 1022.33 872.76 812.12 923.37 1011.63 1020.35 1061.21 1013.27 983.52 1015.32 1042.61 1064.51 1269.03 1237.71 963.08 843.92 781.90 690.03 682.50 602.10 696.08 820.91 787.52 707.08 649.34 664.20 668.50 713.42 684.26 636.00 656.50 605.81 619.93 654.88 673.50 763.31 796.35 914.30 994.83 977.79 956.73 908.42 932.47 913.67 915.07 895.93 826.27 876.35 853.76 827.65 816.91 739.92 697.25 577.60 639.59 618.99 528.58 440.03 416.93 421.04 458.60 458.05 445.50 474.29 554.30 606.50 621.91 570.54 502.19 502.70 395.98 377.12 415.10 510.03 525.10 458.10 344.10 344.88 237.60 236.90 208.07 184.70 218.92 310.10 410.00 581.40 724.24 754.61 876.80 1030.65 1093.61 1111.43 1062.86 1082.70 1074.12 1083.63 1140.90 1228.01 1313.60 1347.50 1342.30 1256.90 1265.07 1297.37 1285.00 1185.03 1134.70 1135.81 1085.82 1037.59 1077.60 1192.77 1115.10 1042.95 1099.04 1111.99 1067.73 1026.41 902.80 736.19 562.85 505.10 526.78 745.95 743.57 816.86 954.23 876.50 870.19 875.00 871.97 777.70 674.89 719.40 717.30 853.78 750.04 748.71 811.35 943.73 1043.49 1201.53 1149.40 914.00
Data Y:
88.0 61.3 295.7 279.0 245.3 195.7 133.3 129.0 138.5 115.3 104.7 108.2 95.7 100.2 117.2 87.0 101.0 103.0 68.0 65.0 55.3 53.0 31.3 27.7 62.3 42.7 18.3 33.3 -8.7 7.3 -12.3 31.7 20.0 13.3 418.7 448.3 433.0 408.7 378.7 368.0 372.7 339.3 287.7 260.7 258.7 224.0 229.7 201.0 216.3 170.3 163.0 139.3 96.7 96.7 123.3 123.3 123.3 126.7 103.3 76.7 66.7 73.3 76.7 68.3 50.0 60.0 53.3 36.7 20.0 20.0 20.0 20.0 20.0 20.0 20.0 20.0 20.0 20.0 20.0 20.0 20.0 20.0 20.0 20.0 20.0 20.0 20.0 20.0 20.0 20.0 20.0 20.0 20.0 20.0 20.0 20.0 20.0 20.0 46.7 53.3 60.0 113.3 76.7 73.3 153.3 156.7 156.7 163.3 123.3 115.0 83.3 58.3 36.7 20.0 26.7 26.7 36.7 41.7 -5.0 63.3 81.7 93.3 100.0 93.3 150.0 160.0 115.0 100.0 110.0 111.7 90.0 86.7 60.0 75.0 76.7 221.7 250.0 286.7 370.0 400.0 400.0 411.7 353.3 313.3 301.7 296.7 355.0 376.7 363.3 348.3 328.3 331.7 316.7 430.0 286.7 250.0 223.3 210.0 186.7 183.3 168.3 156.7 145.0 135.0 116.7 93.3 81.7 75.0 48.3 71.7 58.3 50.0 15.0 -1.7 -13.3 0.0 -1.7 -10.0 -8.3 -11.7 -16.7 -20.0 -25.8 -1.7 5.0 6.7 15.0 43.3 66.7 86.7 95.0 111.7 111.7 138.3 131.7 160.0 131.7 120.0 118.3 119.2 100.0 95.0 78.3 65.0 55.0 131.7 116.7 86.7 73.3 65.0 70.0 70.0 60.0 88.3 73.3 73.3 60.0 48.3 31.7 40.0 11.7 -78.3 -110.0 -108.3 -103.3 -110.0 -95.0 -108.3 -143.3 -108.3 -131.7 -70.0 -76.7 -125.0 -106.7 -90.0 -81.7 -35.0 50.0 90.0 91.7 123.3 98.3 110.0 110.0 138.3 156.7 183.3 200.0 185.0 145.0 123.3 75.0 61.7 46.7 216.7 186.7 161.7 151.7 155.0 136.7 110.0 110.0 86.7 48.3 -10.0 -48.3 -38.3 -33.3 -40.0 -30.0 -18.3 -90.0 -60.0 -51.7 -46.7 10.0 30.0 30.0 23.3 6.7 -21.7 -30.0 -51.7 -56.7 -40.0 -31.7 -31.7 -30.0 -25.0 -10.0 36.7 55.0 63.3 55.0 61.7 61.7 70.0 70.0 80.0 80.0 90.0 100.0 98.3 76.7 70.0 56.7 33.3 -8.3 -25.0 -26.7 -29.2 -30.0 -30.0 73.3 95.0 101.7 78.3 90.0 96.7 155.0 198.3 253.3 280.0 265.0 191.7 140.0 45.0 -51.7 -101.7 -108.3 -140.0 -150.0 -153.3 -145.0 -101.7 -60.0 -25.0 1.7 26.7 28.3 -27.5 -53.3 -58.3 -60.0 -75.0 -53.3 -50.0 -33.3 -33.3 -20.0 -5.0 1.7 -5.0 -16.7 -23.3 -31.7 -36.7 -26.7 -23.3 -81.7 -85.0 6.7 241.7 236.7 228.3 200.0 215.0 221.7 201.7 135.0 91.7 -13.3 -70.0 -61.7 -61.7 -36.7 -158.3 -153.3 -143.3 -98.3 -40.0 -20.0 -53.3 -90.0 -45.0 -36.7 -48.3
Sample Range:
(leave blank to include all observations)
From:
To:
Box-Cox transformation parameter (X series)
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
1
2
Degree (D) of seasonal differencing (X series)
0
1
2
Seasonal Period
1
2
3
4
12
Box-Cox transformation parameter (Y series)
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 (par8=='na.fail') par8 <- na.fail else par8 <- na.pass ccf <- function (x, y, lag.max = NULL, type = c('correlation', 'covariance'), plot = TRUE, na.action = na.fail, ...) { type <- match.arg(type) if (is.matrix(x) || is.matrix(y)) stop('univariate time series only') X <- na.action(ts.intersect(as.ts(x), as.ts(y))) colnames(X) <- c(deparse(substitute(x))[1L], deparse(substitute(y))[1L]) acf.out <- acf(X, lag.max = lag.max, plot = FALSE, type = type, na.action=na.action) lag <- c(rev(acf.out$lag[-1, 2, 1]), acf.out$lag[, 1, 2]) y <- c(rev(acf.out$acf[-1, 2, 1]), acf.out$acf[, 1, 2]) acf.out$acf <- array(y, dim = c(length(y), 1L, 1L)) acf.out$lag <- array(lag, dim = c(length(y), 1L, 1L)) acf.out$snames <- paste(acf.out$snames, collapse = ' & ') if (plot) { plot(acf.out, ...) return(invisible(acf.out)) } else return(acf.out) } 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) print(x) print(y) bitmap(file='test1.png') (r <- ccf(x,y,na.action=par8,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
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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