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Long Range Switching Time Series Prediction via State Space Model

arXiv 2407.19201 ↗First posted 27 Jul 2024cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 29 references that are in the map. First posted on arXiv 27 Jul 2024.

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20 of 29 references.

References · 20

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8 more
  1. 2022On the Parameterization and Initialization of Diagonal State Space Modelscited by 418
  2. 2022Diagonal State Spaces are as Effective as Structured State Spacescited by 350
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  7. 2016Recurrent switching linear dynamical systemscited by 34
  8. 2019Collapsed Amortized Variational Inference for Switching Nonlinear Dynamical Systemscited by 26

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  1. 2021Deep Switching State Space Model (DS3M) for Nonlinear Time Series Forecasting with Regime Switching
  2. 2025A Generalizable Physics-Enhanced State Space Model for Long-Term Dynamics Forecasting in Complex Environments
  3. 2026Inferring Multi-Timescale Neural Dynamics with Switching Linear Dynamical Systems
  4. 2026A Simple State Space Model Excels at Multivariate Time Series Classification
  5. 2024Deep Learning-based Approaches for State Space Models: A Selective Review
  6. 2026Reshape and Recur: Improving SSMs with Input Reshaping and Depth Recurrence
  7. 2024Time-SSM: Simplifying and Unifying State Space Models for Time Series Forecasting
  8. 2026Time-Varying Deep State Space Models for Sequences with Switching Dynamics