Efficient and Effective Time-Series Forecasting with Spiking Neural Networks
<|reference_start|>Efficient and Effective Time-Series Forecasting with Spiking Neural Networks: Spiking neural networks (SNNs), inspired by the spiking behavior of biological neurons, provide a unique pathway for capturing the intricacies of temporal data. However, applying SNNs to time-series forecasting is challenging due to difficulties in effective temporal alignment, complexities in encoding processes, and the absence of standardized guidelines for model selection. In this paper, we propose a framework for SNNs in time-series forecasting tasks, leveraging the efficiency of spiking neurons in processing temporal information. Through a series of experiments, we demonstrate that our proposed SNN-based approaches achieve comparable or superior results to traditional time-series forecasting methods on diverse benchmarks with much less energy consumption. Furthermore, we conduct detailed analysis experiments to assess the SNN's capacity to capture temporal dependencies within time-series data, offering valuable insights into its nuanced strengths and effectiveness in modeling the intricate dynamics of temporal data. Our study contributes to the expanding field of SNNs and offers a promising alternative for time-series forecasting tasks, presenting a pathway for the development of more biologically inspired and temporally aware forecasting models. Our code is available at https://github.com/microsoft/SeqSNN.<|reference_end|>
@article{lv2024efficient,
    title={Efficient and Effective Time-Series Forecasting with Spiking Neural
  Networks},
    author={Changze Lv, Yansen Wang, Dongqi Han, Xiaoqing Zheng, Xuanjing Huang,
  Dongsheng Li},
    journal={arXiv preprint arXiv:2402.01533},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.01533},
    primaryClass={cs.NE}
}