ami and correlation 1.0

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

Requirements:	No special requirements
Platforms:	Matlab
Keyword:	Calculated,  Compute,  Cross,  Distribution,  Nbins,  Number,  Required,  Vector
Users rating:	0/10