AMI computes and plots average mutual information (ami) and correlation of univariate or bivariate time series for different values of time lag.
USAGE:
[amis corrs] = ami(xy,nBins,nLags)

INPUT:
xy: either univariate (x) or bivariate ([x y]) time series data. If bivariate time series are given then x should be independent variable and y should be dependent variable. If univariate time series is given then autocorrelation is calculated instead of cross correlation.

nBins: number of bins for time series data to compute distribution which is required to compute ami. nBins should be either vector of 2 elements (for bivariate) or scalar (univariate).

nLags: number of time lags to compute ami and correlation. Computation is done for lags values of 0:nLags.

OUTPUT:
amis: vector of average mutual information for time lags of 0:nLags

corrs: vector of correlation (or autocorrelation for univariate time seris) for time lags of 0:nLags

EXAMPLES:
xy = rand(1000,2);
nBins = [15 10];
nLags = 25;
[amis corrs]= ami(xy,nBins,nLags);