Independence Analysis

You always wanted to discover which investments or projects are independent to reduce the risks of systematic failures. However you always knew that once the data under analysis has been digitized with a finite number of samples there is no way you could prove the independence.

The traditional solution was based on the usage of Pearson correlation coefficients. However this is in general an unreliable instrument since its purpose is to find how much of the general behavior of one signal can be predicted from a linear translation of the second signal. Even more advanced correlations fail to give an answer because there is no fundamental way of answering this question only with mathematical tools.

To help you with these cases we run an Independent Component Analysis and decide whether the relation between the data sets implies a substantial influence or just noise. The decompositions may even help you predict trends otherwise hard to see.