AMI computes and plots average mutual information (ami) and correlation of univariate or bivariate time series for different values of time lag.
[amis corrs] = ami(xy,nBins,nLags)
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.
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
xy = rand(1000,2);
nBins = [15 10];
nLags = 25;
[amis corrs]= ami(xy,nBins,nLags);
Durga Lal Shrestha (2020). ami and correlation (https://www.mathworks.com/matlabcentral/fileexchange/7936-ami-and-correlation), MATLAB Central File Exchange. Retrieved .
Hello Durga ji Namaskar,
Maile Mimo-OFDM ma fast fading channel ko lagi channel estimation gardaichu. Kehi idea ra kunnai saathi haru chan bhanne contact garaidinus. The job you have done here is very good job.
Useful and efficient.
I want to calculate the correlation coefficient from a matrix of observations?
If you having idea??
Updating description with spelling correction