---
id: 20260529-T0-16
title: "研究发现：简单状态空间模型在多变量时间序列分类中表现优异"
title_en: "Simple State Space Model Excels at Multivariate Time Series Classification"
url: https://ai.daily.yangsir.net/daily/20260529-T0-16
issue_date: 2026-05-29
publish_date: 2026-05-28T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2605.27406
---

# 研究发现：简单状态空间模型在多变量时间序列分类中表现优异

研究者提出一种新型状态空间模型，在多变量时间序列分类任务中超越现有复杂方法。该模型通过结构化状态空间设计（SSM）实现高效序列建模，性能媲美依赖输入状态转换的Mamba架构，但计算成本显著降低。实验证明，这一简化模型在医疗金融等领域的时序数据处理中具备实用价值，为轻量化AI应用提供了新方向。

## English Version

**Simple State Space Model Excels at Multivariate Time Series Classification**

Researchers propose a novel state space model that outperforms complex methods in multivariate time series classification. Using structured state space (SSM) design, it achieves comparable performance to Mamba architectures with significantly lower computational costs. This lightweight model shows practical value for time-series data in healthcare and finance, offering a new direction for efficient AI applications.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2605.27406)

**详情页**：https://ai.daily.yangsir.net/daily/20260529-T0-16

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