基于脉冲神经网络的高效时间序列预测
Efficient and Effective Time-Series Forecasting with Spiking Neural Networks
AI总结：
本文提出一种基于脉冲神经网络（SNN）的时间序列预测框架，利用脉冲神经元高效处理时间信息，在多个基准上以更低能耗达到与传统方法相当或更优的性能，并深入分析了SNN捕捉时间依赖性的能力。
摘要：
脉冲神经网络（SNNs）受生物神经元脉冲行为的启发，为捕捉时间数据的复杂性提供了一条独特途径。然而，由于有效的时间对齐困难、编码过程复杂以及缺乏模型选择的标准化指南，将SNNs应用于时间序列预测具有挑战性。本文提出了一种用于时间序列预测任务的SNNs框架，利用脉冲神经元在处理时间信息方面的效率。通过一系列实验，我们证明了所提出的基于SNN的方法在多样基准上取得了与传统时间序列预测方法相当或更优的结果，且能耗更低。此外，我们进行了详细的分析实验，评估SNN捕捉时间序列数据中时间依赖性的能力，为其在建模时间数据复杂动态方面的细微优势和有效性提供了宝贵见解。我们的研究为SNNs不断扩展的领域做出了贡献，并为时间序列预测任务提供了一种有前景的替代方案，为开发更具生物启发性和时间感知的预测模型铺平了道路。我们的代码可在https://github.com/microsoft/SeqSNN获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
