发表机构
Indian Institute of Technology, Delhi（印度理工学院德里分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对资源受限边缘设备，提出轻量级多尺度自动编码器（LMSAE）网络用于单变量时间序列异常检测。利用离散小波变换提取多尺度特征，采用多尺度损失函数。实验显示其检测性能优、参数少、模型小，适合边缘部署。
AI 中文摘要
时间序列异常检测在物联网系统、传感器网络和边缘监测应用中愈发重要，模型需在内存、延迟和功耗严格限制下运行。虽深度学习方法提高了检测精度，但许多仍计算昂贵且因多尺度敏感度有限无法捕捉细微异常。自动编码器因能很好重建正常模式而广泛用于异常检测。为应对挑战，我们提出用于单变量时间序列异常检测的轻量级多尺度自动编码器（LMSAE）网络，它紧凑且计算高效。LMSAE利用离散小波变换提取多尺度特征并采用多尺度损失函数提高对细微或隐藏异常的敏感度。在基准数据集上的实验表明，其检测性能具有竞争力或更优，参数少且模型大小小于500KB。在NVIDIA Jetson Nano上还实现了低延迟、低功耗推理，推理延迟降低9倍，功耗降低2倍，非常适合边缘部署。
英文摘要
Time-series anomaly detection is increasingly important in IoT systems, sensor networks, and edge monitoring applications, where models must operate under strict constraints on memory, latency, and power consumption. While recent deep-learning approaches have improved detection accuracy, many remain computationally expensive and often fail to capture subtle anomalies due to limited multi-scale sensitivity. Autoencoders are widely used for anomaly detection because they reconstruct normal patterns well, leading to elevated reconstruction errors for anomalous inputs. Their simplicity and efficiency also make them suitable lightweight backbones for handling multi-scale inputs. To address these challenges, we propose a Lightweight MultiScale AutoEncoder (LMSAE) network for univariate time-series anomaly detection, designed to be compact and computationally efficient. LMSAE leverages the Discrete Wavelet Transform (DWT) to extract multi-scale features and employs a multi-scale loss function to improve sensitivity to subtle or hidden anomalies. Experiments on benchmark datasets demonstrate competitive or superior detection performance despite using significantly fewer parameters and a model size of less than 500 KB. LMSAE also achieves low-latency, low-power inference on the NVIDIA Jetson Nano, with 9x reduction in inference latency and 2x reduction in power consumption, making it ideal for edge deployment.
Comments22 pages, 13 figures
