  In this study, we delve into the Structured State Space Model (S4), Change
Point Detection methodologies, and the Switching Non-linear Dynamics System
(SNLDS). Our central proposition is an enhanced inference technique and
long-range dependency method for SNLDS. The cornerstone of our approach is the
fusion of S4 and SNLDS, leveraging the strengths of both models to effectively
address the intricacies of long-range dependencies in switching time series.
Through rigorous testing, we demonstrate that our proposed methodology adeptly
segments and reproduces long-range dependencies in both the 1-D Lorenz dataset
and the 2-D bouncing ball dataset. Notably, our integrated approach outperforms
the standalone SNLDS in these tasks.
