Long Range Switching Time Series Prediction via State Space Model
<|reference_start|>Long Range Switching Time Series Prediction via State Space Model: 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.<|reference_end|>
@article{zhang2024long,
    title={Long Range Switching Time Series Prediction via State Space Model},
    author={Jiaming Zhang, Yang Ding, Yunfeng Gao},
    journal={arXiv preprint arXiv:2407.19201},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.19201},
    primaryClass={cs.LG}
}