Time series models discovery with similarity-based neuro-fuzzy networks and evolutionary algorithms

The discovery of patterns of dependency in heterogeneous multivariate dynamic systems is approached with similarity-based neuro-fuzzy networks and evolutionary algorithms. The search space contains general autoregressive non-linear models representing the dependency structure of the process. Examples show that the proposed approach gives better results than the classical statistical one.