Deep Anomaly Detection on Time Series

时间序列的深度异常检测

• 批准号: 498948972
• 负责人: Professor Dr. Marius Kloft
• 金额: --
• 依托单位: Arbeitsgruppe Maschinelles Lernen
• 依托单位国家: 德国
• 项目类别: Research Units
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/498948972?language=en
• 关键词: Deep; Anomaly; Detection; Time; Series

Time series are ubiquitous, but - in the light of the enormous recent advances enabled by deep learning - anomaly detection (AD) on time series is still in its infancy. In this project, we will develop methods creating modern AD on time series, which is based on deep learning. To this end, we will carry ideas from image AD (such as self-supervised contrastive learning and outlier exposure) over to AD on time series. Such methods rely on a variety of supplemental data (such as the availability of powerful data-augmentation schemes and massive corpora of negative samples) that is notoriously difficult or simply impossible to obtain for time series. As a result, we will study innovative strategies replacing the established supplements. Our approach is based on pragmatically learning the required data augmentations and negative samples. In addition, we will develop methods for AD on time series from the ground up. The proposed model will be based on a random field to model distributions over the gradients of the data. Lastly, we will use contrastive learning to incorporate prior knowledge on chemical processes into our methods, enabling few-shot anomaly detection on chemical process data.

时间序列无处不在，但鉴于深度学习带来的巨大进步，时间序列的异常检测（AD）仍处于起步阶段。在这个项目中，我们将开发基于深度学习的时间序列创建现代AD的方法。为此，我们将把图像AD（例如自监督对比学习和离群值暴露）的想法转移到时间序列上的AD。这种方法依赖于各种补充数据（例如强大的数据增强方案和大量负样本语料库的可用性），而这些数据对于时间序列来说是非常困难或根本不可能获得的。因此，我们将研究创新战略，取代现有的补充剂。我们的方法是基于务实地学习所需的数据扩充和负样本。此外，我们将从头开始开发时间序列上的AD方法。所提出的模型将基于随机场来模拟数据梯度上的分布。 最后，我们将使用对比学习将化学过程的先验知识纳入我们的方法中，从而实现对化学过程数据的少量异常检测。