轻量级状态空间模型在多变量时间序列分类中表现超越复杂模型。
A Simple State Space Model Excels at Multivariate Time Series Classification
- 采用简化状态空间结构，通过线性投影和通道混合提升性能。
- 在59个数据集上优于15个基线模型，参数量仅是对比模型的1/2到1/10。
- 适合追求高效高精度的工业级时序分类任务。
结构化状态空间模型（SSMs）近年来成为序列建模的有力基础，其中基于Mamba的架构通过输入依赖的状态转移展现出强大性能，但复杂度较高。然而，其在时间序列分类（TSC）中的应用主要局限于Mamba类架构，更广泛的SSM设计空间尚未充分探索。本文首次系统性地比较了对角型SSM（S4D）与输入依赖型SSM（Mamba族）在大规模TSC基准上的表现，核心问题是：复杂性是否带来实质性能提升？结果出人意料：S4D在准确率和效率上均持续优于Mamba变体，挑战了复杂性必然带来增益的假设。在此基础上，我们提出MS4——在S4D上引入线性输入投影与通道混合机制的轻量改进；以及MS4N——通过归一化稳定状态动态、开销极低的变体。在包含59个数据集的MONSTER（最多6000万样本、5万时间步、82个类别）和UEA基准上评估，相比15个基线模型，MS4与MS4N始终表现更优，且更为高效；尤其MS4N在性能上达到或超越参数量分别为其2倍和10倍的主流深度学习模型。这些结果表明，轻量级结构化SSM是提升TSC性能的有力替代方案。
原文摘要 · Abstract (English)
Structured state space models (SSMs) have recently emerged as a promising foundation for sequence modeling, with Mamba-based architectures demonstrating strong performance through input-dependent state transitions, albeit at considerable complexity. However, their application to time-series classification (TSC) has been largely limited to Mamba-style architectures, leaving the broader SSM design space underexplored. We present the first systematic study spanning diagonal SSMs (S4D) and input-dependent SSMs (Mamba family) on large-scale TSC benchmarks, asking whether such complexity is necessary for top performance. Our results reveal a surprising finding: S4D consistently outperforms Mamba-based variants in both accuracy and efficiency, challenging the assumption that increased complexity translates to meaningful gains in TSC. Building on this, we introduce MS4, lightweight modifications to S4D via a linear input projection and channel-mixing mechanism, and MS4N, a normalized variant that stabilizes state dynamics with negligible overhead. Evaluated on 59 datasets across MONSTER (up to 60 million samples, 50K timesteps, 82 classes) and the UEA benchmark, against 15 baselines, MS4 and MS4N consistently outperform Mamba-based models while remaining more efficient, and MS4N matches or surpasses competing deep learning models that are roughly 2x and 10x larger in parameters. These results position lightweight structured SSMs as a compelling alternative to scaling complexity for TSC.
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