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.

翻译：脉冲神经网络（SNNs）受生物神经元脉冲发放行为的启发，为捕捉时序数据的复杂特性提供了一条独特途径。然而，将SNNs应用于时间序列预测面临诸多挑战，包括有效时间对齐的困难、编码过程的复杂性以及缺乏模型选择的标准化指导原则。本文提出一个用于时间序列预测任务的SNN框架，充分利用脉冲神经元在处理时序信息时的高效性。通过一系列实验，我们证明所提出的基于SNN的方法在多种基准测试上取得了与传统时间序列预测方法相当或更优的结果，同时能耗显著降低。此外，我们进行了详细的分析实验，以评估SNN捕捉时间序列数据内部时序依赖关系的能力，从而深入揭示其在建模复杂时序动态特性时的细微优势与有效性。本研究推动了SNN这一不断扩展的领域的发展，为时间序列预测任务提供了一个有前景的替代方案，并为开发更具生物启发性和时序感知能力的预测模型指明了方向。我们的代码公开于 https://github.com/microsoft/SeqSNN。