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Data X:
107 131 145 139 148 132 134 360 142 135 166 152 122 102 124 131 137 141 163 249 137 178 171 175 157 47 104 164 148 164 165 156 159 117 188 198 162 153 154 177 189 138 154 152 122 166 160 165 132 161 149 175
Data Y:
9 18 18 17 12 31 11 12 18 14 21 17 13 22 16 14 22 14 17 13 12 14 28 21 17 2 4 25 19 22 9 6 24 20 18 27 15 15 13 14 24 6 23 21 25 28 18 19 12 16 13 13
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')
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Big Analytics Cloud Computing Center
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