Dependency derivation in industrial process data
Daniel Gillblad, A. Holst · 2002
In many industrial processes, finding dependencies and the creation of dependency graphs can increase the understanding of the system significantly. This knowledge can then be used for further optimization and variable selection. Most of the measured attributes in these cases come in the form of time series. There are several ways of determining correlation between series, most of them suffering from specific problems when applied to real-world data. Here, a well performing measure based on the mutual information rate is derived and discussed with results from both synthetic and real data.