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.

翻译：脉冲神经网络（SNNs）受生物神经元脉冲行为启发，为捕捉时序数据的复杂性提供了独特路径。然而，由于有效时间对齐困难、编码过程复杂以及缺乏标准化模型选择准则，将SNNs应用于时间序列预测仍面临挑战。本文提出了一种面向时间序列预测任务的SNNs框架，利用脉冲神经元处理时序信息的高效性。通过系列实验表明，所提出的基于SNN的方法在多个基准数据集上能以显著更低的能耗取得与传统时间序列预测方法相当甚至更优的结果。此外，我们开展详细分析实验评估SNN捕捉时序数据中时间依赖关系的能力，为揭示其建模时序数据复杂动态特征的细微优势与有效性提供了宝贵见解。本研究拓展了SNNs领域，并为时间序列预测任务提供了富有前景的替代方案，开辟了更具生物启发性和时间感知能力的预测模型发展路径。