English(EN) HAN-Mamba: Hierarchical Selective State Space Networks for Multi-Scale Financial Volatility Forecasting

HAN-Mamba模型利用Mamba架构增强金融波动率预测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-08 04:00

研究人员开发了HAN-Mamba，这是一种新颖的混合架构，它将选择性状态空间模型（Mamba）与分层结构相结合，以改进金融波动率预测。该模型用Mamba编码器取代了其前身HAN-T中计算量大的Transformer编码器，从而显著减少了参数数量，并在Optiver已实现波动率预测基准测试中提高了性能。HAN-Mamba通过扩展其高频上下文处理能力并实现恒定时间流更新，展示了更高的准确性，优于其基于注意力机制的对应模型。 AI

影响这种混合架构可以为金融和其他领域的时序数据提供更有效、更准确的预测。

排序理由该项目是一篇详细介绍新模型架构的研究论文。[lever_c_research降级：ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Mihai Bogdan Deaconu, Ioan Daniel Pop · 2026-10-08 04:00

HAN-Mamba：用于多尺度金融波动预测的分层选择性状态空间网络

arXiv:2610.10323v1 Announce Type: new Abstract: Short-horizon realized volatility forecasting requires the integration of market information that evolves at incompatible temporal resolutions, from second-level order book dynamics to weekly regime drift. Our conference work introd…

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HAN-Mamba：用于多尺度金融波动预测的分层选择性状态空间网络

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