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Naga: Vedic Encoding for Deep State Space Models

arXiv 2511.13510 ↗First posted 17 Nov 2025cs.LG cs.AI cs.SYcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 26 of its 37 references that are in the map. First posted on arXiv 17 Nov 2025.

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References · 26

  1. 2016Layer Normalizationcited by 5,669
  2. 2017Squeeze-and-Excitation Networkscited by 4,789
  3. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,718
  4. 2021Efficiently Modeling Long Sequences with Structured State Spacescited by 2,465
  5. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,169
  6. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 2,039
  7. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,676
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  10. 2024Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Dualitycited by 1,336
  11. 2017Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networkscited by 879
  12. 2020A Survey on Visual Transformercited by 654
14 more
  1. 2016Temporal Convolutional Networks for Action Segmentation and Detectioncited by 508
  2. 2021SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interactioncited by 338
  3. 2023Long-term Forecasting with TiDE: Time-series Dense Encodercited by 332
  4. 2022FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecastingcited by 147
  5. 2024Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Seriescited by 127
  6. 2018Global Second-order Pooling Convolutional Networkscited by 66
  7. 2019Learning Deep Bilinear Transformation for Fine-grained Image Representationcited by 54
  8. 2024xPatch: Dual-Stream Time Series Forecasting with Exponential Seasonal-Trend Decompositioncited by 36
  9. 2023Mixture-of-Linear-Experts for Long-term Time Series Forecastingcited by 29
  10. 2023PatchMixer: A Patch-Mixing Architecture for Long-Term Time Series Forecastingcited by 20
  11. 2022First De-Trend then Attend: Rethinking Attention for Time-Series Forecastingcited by 19
  12. 2024PRformer: Pyramidal Recurrent Transformer for Multivariate Time Series Forecastingcited by 5
  13. 2021Long-term series forecasting with Query Selector -- efficient model of sparse attentioncited by 4
  14. 2024Disentangled Parameter-Efficient Linear Model for Long-Term Time Series Forecastingcited by 4

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  1. 2024Deep Learning-based Approaches for State Space Models: A Selective Review
  2. 2023Effectively Modeling Time Series with Simple Discrete State Spaces
  3. 2024SDE: A Simplified and Disentangled Dependency Encoding Framework for State Space Models in Time Series Forecasting
  4. 2022Interpretable Latent Variables in Deep State Space Models
  5. 2024State Space Models as Foundation Models: A Control Theoretic Overview
  6. 2022Deep Latent State Space Models for Time-Series Generation
  7. 2026A Decomposition-based State Space Model for Multivariate Time-Series Forecasting
  8. 2024Mamba-360: Survey of State Space Models as Transformer Alternative for Long Sequence Modelling: Methods, Applications, and Challenges