mlsanityData to 14 Oct 2026

TS-LIF: A Temporal Segment Spiking Neuron Network for Time Series Forecasting

arXiv 2503.05108 ↗First posted 7 Mar 2025cs.LG cs.AIcited by 6

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Abstract (arXiv)

Spiking Neural Networks (SNNs) offer a promising, biologically inspired approach for processing spatiotemporal data, particularly for time series forecasting. However, conventional neuron models like the Leaky Integrate-and-Fire (LIF) struggle to capture long-term dependencies and effectively process multi-scale temporal dynamics. To overcome these limitations, we introduce the Temporal Segment Leaky Integrate-and-Fire (TS-LIF) model, featuring a novel dual-compartment architecture. The dendritic and somatic compartments specialize in capturing distinct frequency components, providing functional heterogeneity that enhances the neuron's ability to process both low- and high-frequency information. Furthermore, the newly introduced direct somatic current injection reduces information loss during intra-neuronal transmission, while dendritic spike generation improves multi-scale information extraction. We provide a theoretical stability analysis of the TS-LIF model and explain how each compartment contributes to distinct frequency response characteristics. Experimental results show that TS-LIF outperforms traditional SNNs in time series forecasting, demonstrating better accuracy and robustness, even with missing data. TS-LIF advances the application of SNNs in time-series forecasting, providing a biologically inspired approach that captures complex temporal dynamics and offers potential for practical implementation in diverse forecasting scenarios. The source code is available at https://github.com/kkking-kk/TS-LIF.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 24 of its 48 references that are in the map. First posted on arXiv 7 Mar 2025.

Builds on

24 of 48 references.

References · 24

  1. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
  2. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,169
  3. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,676
  4. 2018An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modelingcited by 1,476
  5. 2023iTransformer: Inverted Transformers Are Effective for Time Series Forecastingcited by 931
  6. 2017Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networkscited by 879
  7. 2024Unified Training of Universal Time Series Forecasting Transformerscited by 614
  8. 2016Temporal Convolutional Networks for Action Segmentation and Detectioncited by 508
  9. 2022Attention Spiking Neural Networkscited by 107
  10. 2024SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameterscited by 107
  11. 2023Conformal PID Control for Time Series Predictioncited by 88
  12. 2022GLIF: A Unified Gated Leaky Integrate-and-Fire Neuron for Spiking Neural Networkscited by 63
12 more
  1. 2015Why Neurons Have Thousands of Synapses, A Theory of Sequence Memory in Neocortexcited by 45
  2. 2024SAMformer: Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attentioncited by 44
  3. 2022TCJA-SNN: Temporal-Channel Joint Attention for Spiking Neural Networkscited by 35
  4. 2023TC-LIF: A Two-Compartment Spiking Neuron Model for Long-Term Sequential Modellingcited by 21
  5. 2023Complex Dynamic Neurons Improved Spiking Transformer Network for Efficient Automatic Speech Recognitioncited by 20
  6. 2024High-Performance Temporal Reversible Spiking Neural Networks with O(L) Training Memory and O(1) Inference Costcited by 14
  7. 2024Efficient and Effective Time-Series Forecasting with Spiking Neural Networkscited by 12
  8. 2020Markovian RNN: An Adaptive Time Series Prediction Network with HMM-based Switching for Nonstationary Environmentscited by 9
  9. 2024Autaptic Synaptic Circuit Enhances Spatio-temporal Predictive Learning of Spiking Neural Networkscited by 8
  10. 2025HDT: Hierarchical Discrete Transformer for Multivariate Time Series Forecastingcited by 7
  11. 2023Learning with Chemical versus Electrical Synapses -- Does it Make a Difference?cited by 5
  12. 2022Beyond spiking networks: the computational advantages of dendritic amplification and input segregationcited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 4.

6 papers.

  1. 2025SpikeSTAG: Spatial-Temporal Forecasting via GNN-SNN Collaborationcited by 3
  2. 2025Edge Intelligence with Spiking Neural Networkscited by 2
  3. 2026SpikySpace: A Spiking State Space Model for Energy-Efficient Time Series Forecastingcited by 0
  4. 2026Detecting AI-Generated Videos with Spiking Neural Networkscited by 0
  5. 2026SpikF-GO: Spiking Fourier Graph Operators for Multivariate Time Series Forecastingcited by 0
  6. 2026SpikeLite: Lightweight Spiking Neural Networks for Time-Series Forecastingcited by 0