English(EN) Naga: Vedic Encoding for Deep State Space Models

Naga：受吠陀数学启发的模型增强时间序列预测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

研究人员推出了一种新颖的深度状态空间模型（SSM），名为 Naga，该模型利用吠陀数学的概念进行时间序列预测。该方法处理序列的双向性，并通过 Hadamard 交互进行组合，增强了模型捕获长期时间依赖关系的能力。Naga 在多个基准测试中表现出色，优于 28 种现有最先进的模型，并显示出比其他深度 SSM 方法更高的效率。 AI

影响引入了一种新颖的时间序列预测编码方法，提高了效率和准确性。

排序理由该集群包含一篇详细介绍新模型架构的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Melanie Schaller, Nick Janssen, Bodo Rosenhahn · 2026-10-15 04:00

Naga：用于深度状态空间模型的吠陀编码

arXiv:2511.13510v2 Announce Type: replace-cross Abstract: This paper presents Naga, a deep State Space Model (SSM) encoding approach inspired by structural concepts from Vedic mathematics. The proposed method introduces a bidirectional representation for time series by jointly pr…

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Naga：用于深度状态空间模型的吠陀编码

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