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Data X:
87 173 130 1169 2251 2078 2554 3896 390 693 10000 8355 5628 7143 6710 909 606 9740 6494 3983 4935 3636 216 216 6277 3117 2597 2857 2511 0 87 4372 2294 1255 1775 216 130 43 0 2251 1212 1515 1039 0 346 2251 1082 866 1342 519 87 0 1082 952 736 952 87 130 0 563 996 649 996 519 0 43 1039 1342 952 649 823 130 0 909 519 303 0 260 0 43 823 303
Data Y:
180 188 346 265 492 578 1124 1341 1333 1751 2104 3022 4269 4317 6531 3629 3619 6033 5676 6248 9541 10000 9843 6837 6419 7101 8904 9827 9181 7115 5814 4691 6185 8125 7124 5677 4732 3465 3199 3084 3668 4248 5335 3356 2911 1908 2002 3166 3579 2697 2184 1813 1425 1665 2347 2120 1540 1284 1005 704 1233 1666 1829 1670 1062 801 1005 858 1337 1265 1044 786 587 920 776 1015 707 998 394 493 665 720
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
1
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.fail
na.pass
Chart options
Label y-axis:
Label x-axis:
R Code
par8 <- 'na.fail' par7 <- '0' par6 <- '0' par5 <- '1' par4 <- '1' par3 <- '0' par2 <- '0' par1 <- '1' 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
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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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