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

arXiv: https://arxiv.org/abs/2503.05108 · HTML: https://mlsanity.com/paper/2503.05108/

## 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 (most cited first, 24 of 24)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://mlsanity.com/paper/2106.13008/) (2021)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [iTransformer: Inverted Transformers Are Effective for Time Series Forecasting](https://mlsanity.com/paper/2310.06625/) (2023)
- [Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks](https://mlsanity.com/paper/1703.07015/) (2017)
- [Unified Training of Universal Time Series Forecasting Transformers](https://mlsanity.com/paper/2402.02592/) (2024)
- [Temporal Convolutional Networks for Action Segmentation and Detection](https://mlsanity.com/paper/1611.05267/) (2016)
- [Attention Spiking Neural Networks](https://mlsanity.com/paper/2209.13929/) (2022)
- [SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters](https://mlsanity.com/paper/2405.00946/) (2024)
- [Conformal PID Control for Time Series Prediction](https://mlsanity.com/paper/2307.16895/) (2023)
- [GLIF: A Unified Gated Leaky Integrate-and-Fire Neuron for Spiking Neural Networks](https://mlsanity.com/paper/2210.13768/) (2022)
- [Why Neurons Have Thousands of Synapses, A Theory of Sequence Memory in Neocortex](https://mlsanity.com/paper/1511.00083/) (2015)
- [SAMformer: Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention](https://mlsanity.com/paper/2402.10198/) (2024)
- [TCJA-SNN: Temporal-Channel Joint Attention for Spiking Neural Networks](https://mlsanity.com/paper/2206.10177/) (2022)
- [TC-LIF: A Two-Compartment Spiking Neuron Model for Long-Term Sequential Modelling](https://mlsanity.com/paper/2308.13250/) (2023)
- [Complex Dynamic Neurons Improved Spiking Transformer Network for Efficient Automatic Speech Recognition](https://mlsanity.com/paper/2302.01194/) (2023)
- [High-Performance Temporal Reversible Spiking Neural Networks with O(L) Training Memory and O(1) Inference Cost](https://mlsanity.com/paper/2405.16466/) (2024)
- [Efficient and Effective Time-Series Forecasting with Spiking Neural Networks](https://mlsanity.com/paper/2402.01533/) (2024)
- [Markovian RNN: An Adaptive Time Series Prediction Network with HMM-based Switching for Nonstationary Environments](https://mlsanity.com/paper/2006.10119/) (2020)
- [Autaptic Synaptic Circuit Enhances Spatio-temporal Predictive Learning of Spiking Neural Networks](https://mlsanity.com/paper/2406.00405/) (2024)
- [HDT: Hierarchical Discrete Transformer for Multivariate Time Series Forecasting](https://mlsanity.com/paper/2502.08302/) (2025)
- [Learning with Chemical versus Electrical Synapses -- Does it Make a Difference?](https://mlsanity.com/paper/2401.08602/) (2023)
- [Beyond spiking networks: the computational advantages of dendritic amplification and input segregation](https://mlsanity.com/paper/2211.02553/) (2022)

## Built on by (most cited first, 6 of 6)

- [SpikeSTAG: Spatial-Temporal Forecasting via GNN-SNN Collaboration](https://mlsanity.com/paper/2508.02069/) (2025)
- [Edge Intelligence with Spiking Neural Networks](https://mlsanity.com/paper/2507.14069/) (2025)
- [SpikySpace: A Spiking State Space Model for Energy-Efficient Time Series Forecasting](https://mlsanity.com/paper/2601.02411/) (2026)
- [Detecting AI-Generated Videos with Spiking Neural Networks](https://mlsanity.com/paper/2605.05895/) (2026)
- [SpikF-GO: Spiking Fourier Graph Operators for Multivariate Time Series Forecasting](https://mlsanity.com/paper/2606.13901/) (2026)
- [SpikeLite: Lightweight Spiking Neural Networks for Time-Series Forecasting](https://mlsanity.com/paper/2609.35097/) (2026)

## 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.