多变量时间序列的在线模型基异常检测：分类、综述、研究挑战与未来方向
摘要
时间序列异常检测在工程过程中发挥着重要作用，例如开发、制造以及其他涉及动态系统的作业。这些过程可以从该领域的最新进展中获益，由于现代方法可能有助于应对高度维数据等情况。在为读者提供术语理解方面，本综述引入了一种新分类法，其中对在线与离线以及训练与推断进行了区分。此外，本综述呈现了文献中最常用的数据集和评估指标，以及详细分析。 Furthermore, this survey provides an extensive overview of the state-of-the-art model-based online semi- and unsupervised anomaly detection approaches for multivariate time-series data, categorising them into different model families and other properties. The biggest research challenge revolves around benchmarking, as currently there is no reliable way to compare different approaches against one another. This problem is two-fold: on the one hand, public data sets suffers from at least one fundamental flaw, while on the other hand, there is a lack of intuitive and representative evaluation metrics in the field. Moreover, the way most publications choose a detection threshold disregards real-world conditions, which hinders the application in the real world. To allow for tangible advances in the field, these issues must be addressed in future work.
引用
@article{arxiv.2408.03747,
title = {Online Model-based Anomaly Detection in Multivariate Time Series: Taxonomy, Survey, Research Challenges and Future Directions},
author = {Lucas Correia and Jan-Christoph Goos and Philipp Klein and Thomas Bäck and Anna V. Kononova},
journal= {arXiv preprint arXiv:2408.03747},
year = {2024}
}