arXiv：cs.AI· Melanie Schaller, Nick Janssen, Bodo Rosenhahn·· 16 小时前
Naga：受吠陀数学启发的深度状态空间模型编码方法
Naga: Vedic Encoding for Deep State Space Models
AI 导读
研究者提出 Naga，一种受吠陀数学结构概念启发的深度状态空间模型（SSM）编码方法，通过双向表示联合处理正向与时间反转的输入序列，并经逐元素（Hadamard）交互融合，增强模型捕捉远距离时间依赖的能力。
正文
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 processing forward and time-reversed input sequences. These representations are then combined through an element-wise (Hadamard) interaction, resulting in a Vedic-inspired encoding that enhances the model's ability to capture temporal dependencies across distant time steps. We evaluate Naga on multiple long-term time series forecasting (LTSF) benchmarks, including ETTh1, ETTh2, ETTm1, ETTm2, Weather, Traffic, and ILI. The experimental results show that Naga outperforms 28 current state of the art models and demonstrates improved efficiency compared to existing deep SSM-based approaches. The findings suggest that incorporating structured, Vedic-inspired decomposition can provide an interpretable and computationally efficient alternative for long-range sequence modeling.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Systems and Control (eess.SY) |
| Cite as: | arXiv:2511.13510 [cs.LG] |
| (or arXiv:2511.13510v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2511.13510 arXiv-issued DOI via DataCite |
Submission history
From: Melanie Schaller Dr. [view email]
[v1]
Mon, 17 Nov 2025 15:43:49 UTC (436 KB)
[v2]
Mon, 12 Oct 2026 15:12:47 UTC (429 KB)
来源：arXiv：cs.AI · arxiv.org