Online Sparse Identification of Dynamical Systems with Regime Switching by Causation Entropy Boosting

Online nonlinear system identification with sequential data has recently become important in many applications, e.g., extreme weather events, climate change, and autonomous systems. In this work, we developed a causation entropy boosting (CEBoosting) framework for online nonlinear system identification. For each sequential data batch, this framework calculates the causation entropy that evaluates the contribution of each function in a large set of candidate functions to the system dynamics. The causation entropies based on multiple data batches are then aggregated to identify a few candidate functions that have significant impacts on the system dynamics. With the identified sparse set of functions, the framework further fits a model of the system dynamics. The results show that the CEBoosting method can capture the regime switching and then fit models of system dynamics for various types of complex dynamical based on a limited amount of sequential data.