单层Mamba经优化后在时序分类上表现超群，且可复现。
MambaSL: Exploring Single-Layer Mamba for Time Series Classification
- 基于时序分类需求重设计单层Mamba的选通状态空间与投影层
- 在全部30个UEA数据集上实现领先性能，平均提升显著
- 公开所有模型检查点，确保结果可复现，适合研究者参考
尽管状态空间模型（SSMs）如Mamba在多个序列任务中取得进展，但其在时序分类（TSC）中的独立能力研究仍有限。本文提出MambaSL，通过四个面向TSC的假设，对单层Mamba的选通状态空间和投影层进行最小化重构。为解决基准测试的局限性——配置受限、仅部分覆盖东安格利亚大学（UEA）数据集、设置不可复现——我们采用统一协议，在全部30个UEA数据集上重新评估20个强基线模型。结果显示，MambaSL在统计意义上实现最优平均性能，并通过公开所有模型检查点确保可复现性。结合可视化分析，这些成果证明了基于Mamba的架构作为TSC主干网络的巨大潜力。
原文摘要 · Abstract (English)
Despite recent advances in state space models (SSMs) such as Mamba across various sequence domains, research on their standalone capacity for time series classification (TSC) has remained limited. We propose MambaSL, a framework that minimally redesigns the selective SSM and projection layers of a single-layer Mamba, guided by four TSC-specific hypotheses. To address benchmarking limitations -- restricted configurations, partial University of East Anglia (UEA) dataset coverage, and insufficiently reproducible setups -- we re-evaluate 20 strong baselines across all 30 UEA datasets under a unified protocol. As a result, MambaSL achieves state-of-the-art performance with statistically significant average improvements, while ensuring reproducibility via public checkpoints for all evaluated models. Together with visualizations, these results demonstrate the potential of Mamba-based architectures as a TSC backbone.
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