mlsanityData to 7 Oct 2026

SpikySpace: A Spiking State Space Model for Energy-Efficient Time Series Forecasting

arXiv 2601.02411 ↗First posted 2 Jan 2026cs.NE cs.AI cs.LGcited by 0

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Abstract (arXiv)

Time-series forecasting in domains like traffic management and industrial monitoring often requires real-time, energy-efficient processing on edge devices with limited resources. Spiking neural networks (SNNs) offer event-driven computation and ultra-low power and have been proposed for use in this space. Unfortunately, existing SNN-based time-series forecasters often use complex transformer blocks. To address this issue, we propose SpikySpace, a spiking state-space model (SSM) that reduces the quadratic cost in the attention block to linear time via spiking selective scanning. Further, we introduce PTsoftplus and PTSiLU, two efficient approximations of SiLU and Softplus that replace costly exponential and division operations with simple bit-shifts. Evaluated on four multivariate time-series benchmarks, SpikySpace outperforms the leading SNN in terms of accuracy by up to 3.0% while reducing energy consumption by over 96.1%. As the first fully spiking state-space model, SpikySpace bridges neuromorphic efficiency with modern sequence modeling, opening a practical path toward efficient time series forecasting systems. Our code is available at https://anonymous.4open.science/r/SpikySpace.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 29 of its 42 references that are in the map. First posted on arXiv 2 Jan 2026.

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29 of 42 references.

References · 29

  1. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,616
  2. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  3. 2021Efficiently Modeling Long Sequences with Structured State Spacescited by 2,424
  4. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,341
  5. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,141
  6. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 1,981
  7. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,654
  8. 2023iTransformer: Inverted Transformers Are Effective for Time Series Forecastingcited by 906
  9. 2017Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networkscited by 869
  10. 2019Surrogate Gradient Learning in Spiking Neural Networkscited by 604
  11. 2016Temporal Convolutional Networks for Action Segmentation and Detectioncited by 504
  12. 2017A Survey of Model Compression and Acceleration for Deep Neural Networkscited by 298
17 more
  1. 2023Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networkscited by 116
  2. 2019Understanding LSTM -- a tutorial into Long Short-Term Memory Recurrent Neural Networkscited by 103
  3. 2022BiT: Robustly Binarized Multi-distilled Transformercited by 51
  4. 2023Fast-SNN: Fast Spiking Neural Network by Converting Quantized ANNcited by 42
  5. 2022The BrainScaleS-2 accelerated neuromorphic system with hybrid plasticitycited by 41
  6. 2024Direct Training High-Performance Deep Spiking Neural Networks: A Review of Theories and Methodscited by 24
  7. 2024SpikingSSMs: Learning Long Sequences with Sparse and Parallel Spiking State Space Modelscited by 19
  8. 2024Efficient and Effective Time-Series Forecasting with Spiking Neural Networkscited by 12
  9. 2024Autonomous Driving with Spiking Neural Networkscited by 12
  10. 2024P-SpikeSSM: Harnessing Probabilistic Spiking State Space Models for Long-Range Dependency Taskscited by 12
  11. 2024Reconsidering the Energy Efficiency of Spiking Neural Networks Inference from Analytical Perspectivescited by 12
  12. 2024SPikE-SSM: A Sparse, Precise, and Efficient Spiking State Space Model for Long Sequences Learningcited by 9
  13. 2024Sorbet: A Neuromorphic Hardware-Compatible Transformer-Based Spiking Language Modelcited by 6
  14. 2025TS-LIF: A Temporal Segment Spiking Neuron Network for Time Series Forecastingcited by 6
  15. 2022Low Latency Conversion of Artificial Neural Network Models to Rate-encoded Spiking Neural Networkscited by 5
  16. 2025SpikeSTAG: Spatial-Temporal Forecasting via GNN-SNN Collaborationcited by 3
  17. 2025SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mambacited by 3

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  3. 2023Effectively Modeling Time Series with Simple Discrete State Spaces
  4. 2026ASGMamba: Adaptive Spectral Gating Mamba for Multivariate Time Series Forecasting
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