真空尖峰器：一种基于脉冲神经网络的高效时间序列异常检测模型
Vacuum Spiker: A Spiking Neural Network-Based Model for Efficient Anomaly Detection in Time Series
AI总结：
本文提出真空尖峰器算法，一种基于脉冲神经网络的时间序列异常检测方法，通过神经活动全局变化和高效编码实现低能耗检测，在公开数据集和太阳能逆变器案例中验证了性能与节能优势。
摘要：
异常检测是工业、医疗保健和网络安全等领域的一项关键任务。许多现实世界的异常检测问题涉及随时间分析多个特征，这使得时间序列分析成为解决此类问题的自然方法。虽然深度学习模型在该领域取得了强劲的性能，但其高能耗的趋势限制了它们在资源受限环境（如物联网设备、边缘计算平台和可穿戴设备）中的部署。为了应对这一挑战，本文引入了真空尖峰器算法，一种新颖的基于脉冲神经网络的时间序列异常检测方法。它采用了一种新的检测标准，该标准依赖于神经活动的全局变化，而非重建或预测误差。它以一种新颖的方式使用尖峰时间依赖可塑性进行训练，旨在在异常发生时诱发神经活动的变化。本文还提出了一种新的高效编码方案，该方案将输入空间离散化为不重叠的区间，并将每个区间分配给单个神经元。这种策略每个时间步仅用一个尖峰编码信息，相比传统编码方法提高了能量效率。在公开数据集上的实验结果表明，与广泛的深度学习和机器学习基线相比，所提出的算法在显著降低能耗的同时实现了具有竞争力的性能。此外，其实际效用在一个真实世界案例研究中得到了验证，该模型成功识别了太阳能逆变器中的功率削减事件。这些结果凸显了其在可持续和高效异常检测方面的潜力。
英文摘要：
Anomaly detection is a key task across domains such as industry, healthcare, and cybersecurity. Many real-world anomaly detection problems involve analyzing multiple features over time, making time series analysis a natural approach for such problems. While deep learning models have achieved strong performance in this field, their trend to exhibit high energy consumption limits their deployment in resource-constrained environments such as IoT devices, edge computing platforms, and wearables. To address this challenge, this paper introduces the \textit{Vacuum Spiker algorithm}, a novel Spiking Neural Network-based method for anomaly detection in time series. It incorporates a new detection criterion that relies on global changes in neural activity rather than reconstruction or prediction error. It is trained using Spike Time-Dependent Plasticity in a novel way, intended to induce changes in neural activity when anomalies occur. A new efficient encoding scheme is also proposed, which discretizes the input space into non-overlapping intervals, assigning each to a single neuron. This strategy encodes information with a single spike per time step, improving energy efficiency compared to conventional encoding methods. Experimental results on publicly available datasets show that the proposed algorithm achieves competitive performance while significantly reducing energy consumption, compared to a wide set of deep learning and machine learning baselines. Furthermore, its practical utility is validated in a real-world case study, where the model successfully identifies power curtailment events in a solar inverter. These results highlight its potential for sustainable and efficient anomaly detection.
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。
