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Naga: Vedic Math-Inspired Model Enhances Time Series Forecasting

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced Naga, a novel deep State Space Model (SSM) that utilizes concepts from Vedic mathematics for time series forecasting. This approach processes sequences bidirectionally and combines them using a Hadamard interaction, enhancing the model's ability to capture long-range temporal dependencies. Naga has demonstrated superior performance on multiple benchmarks, outperforming 28 existing state-of-the-art models and showing improved efficiency over other deep SSM methods. AI

IMPACT Introduces a novel encoding method for time series forecasting that improves efficiency and accuracy.

RANK_REASON The cluster contains a research paper detailing a new model architecture. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Naga: Vedic Math-Inspired Model Enhances Time Series Forecasting

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19 / 100
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The cluster contains a research paper detailing a new model architecture. [lever_c_demoted from research: ic=1 ai=1.0]
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High
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Breaking (< 6h)
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Melanie Schaller, Nick Janssen, Bodo Rosenhahn ·

    Naga: Vedic Encoding for Deep State Space Models

    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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