Evolutionary learning of dynamic probabilistic models with large time lags

In this paper, we explore the automatic explanation of Multivariate Time Series (MTS) through learning Dynamic Bayesian Networks (DBNs). We have developed an evolutionary algorithm which exploits certain characteristics of process MTS in order to generate good networks as quickly as possible. We compare this algorithm to other standard learning algorithms that have traditionally been used for static Bayesian networks but are adapted for DBNs in this paper. These are tested on both synthetic and real-world MTS. We evaluate sample explanations which have been generated from chemical process data using our methodology, and several useful heuristics, we have found that the proposed method is more efficient for learning DBNs from MTS with large time lags, especially in time-demanding situations.