Long Range Switching Time Series Prediction via State Space Model

TL;DR

This paper introduces an improved method combining Structured State Space Models and Switching Non-linear Dynamics Systems to better capture long-range dependencies in time series data, demonstrated on Lorenz and bouncing ball datasets.

Contribution

We propose a novel fusion of S4 and SNLDS models, enhancing inference and long-range dependency modeling in switching time series.

Findings

Outperforms standalone SNLDS in segmentation and reproduction tasks

Effective in modeling long-range dependencies in Lorenz and bouncing ball datasets

Demonstrates robustness and improved accuracy over existing methods

Abstract

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.