使用可学习数据增强的自监督时间序列异常检测
Self-Supervised Time-Series Anomaly Detection Using Learnable Data Augmentation
AI总结：
提出自监督时间序列异常检测技术LATAD，通过对比学习和可学习数据增强生成负样本提取特征，基于潜在特征相似度计算异常分数，性能优于现有方法并能诊断根本原因。
摘要：
为提升工业现场的生产力和安全性，人们在推进各种制造过程的异常检测方面不断努力。深度学习取代了基于规则的方法，近期已成为各行业异常检测的有前景的方法。然而在现实世界中，异常数据的稀缺和获取标注数据的困难，给检测模型的训练造成了限制。在本研究中，我们通过提出一种基于可学习数据增强的时间序列异常检测（LATAD）技术来解决这些缺陷，该技术以自监督方式进行训练。LATAD通过对比学习从时间序列数据中提取判别性特征。同时，可学习数据增强生成具有挑战性的负样本以提升学习效率。我们基于潜在特征相似度测量了该技术的异常分数。结果表明，LATAD在几个基准数据集上表现出与最先进的异常检测评估相当或更优的性能，并提供了一种基于梯度的诊断技术以帮助识别根本原因。
英文摘要：
Continuous efforts are being made to advance anomaly detection in various manufacturing processes to increase the productivity and safety of industrial sites. Deep learning replaced rule-based methods and recently emerged as a promising method for anomaly detection in diverse industries. However, in the real world, the scarcity of abnormal data and difficulties in obtaining labeled data create limitations in the training of detection models. In this study, we addressed these shortcomings by proposing a learnable data augmentation-based time-series anomaly detection (LATAD) technique that is trained in a self-supervised manner. LATAD extracts discriminative features from time-series data through contrastive learning. At the same time, learnable data augmentation produces challenging negative samples to enhance learning efficiency. We measured anomaly scores of the proposed technique based on latent feature similarities. As per the results, LATAD exhibited comparable or improved performance to the state-of-the-art anomaly detection assessments on several benchmark datasets and provided a gradient-based diagnosis technique to help identify root causes.
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。
