Long Range Switching Time Series Prediction via State Space Model
This work addresses the challenge of modeling complex, switching dynamics in time series data, which is incremental as it combines existing models to improve performance on specific tasks.
The paper tackled the problem of predicting long-range dependencies in switching time series by proposing an enhanced inference technique that fuses Structured State Space Models (S4) and Switching Non-linear Dynamics Systems (SNLDS). The result showed that this integrated approach outperformed standalone SNLDS in segmenting and reproducing dependencies on 1-D Lorenz and 2-D bouncing ball datasets.
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.