Dynamic Bayesian networks (DBNs) are a class of probabilistic graphical models that has become a standard tool for modeling various stochastic time-varying phenomena. Probabilistic graphical models such as 2-Time slice BN (2TBNs) are the most used and popular models for DBNs. Because of the complexity induced by adding the temporal dimension, DBN structure learning is a very complex task. Existing algorithms are adaptations of score-based BN structure learning algorithms but are often limited when the number of variables is high. Another limitation of DBN structure learning studies, they use their own benchmarks and techniques for evaluation. The problem in the dynamic case is that we don't find previous works that provide details about used networks and indicators of comparison. We focus in this project on DBN structure learning and its methods of evaluation with respectively another family of structure learning algorithms, local search methods, known by its scalability and a novel approach to generate large standard DBNs and metric of evaluation. We illustrate the interest of these methods with experimental results
