Multivariate Time Series (MVTS) anomaly detection is a long-standing and challenging research topic that has attracted tremendous research effort from both industry and academia recently. However, a careful study of the literature makes us realize that 1) the community is active but not as organized as other sibling machine learning communities such as Computer Vision (CV) and Natural Language Processing (NLP), and 2) most proposed solutions are evaluated using either inappropriate or highly flawed protocols, with an apparent lack of scientific foundation. So flawed is one very popular protocol, the so-called \pa protocol, that a random guess can be shown to systematically outperform \emph{all} algorithms developed so far. In this paper, we review and evaluate many recent algorithms using more robust protocols and discuss how a normally good protocol may have weaknesses in the context of MVTS anomaly detection and how to mitigate them. We also share our concerns about benchmark datasets, experiment design and evaluation methodology we observe in many works. Furthermore, we propose a simple, yet challenging, baseline algorithm based on Principal Components Analysis (PCA) that surprisingly outperforms many recent Deep Learning (DL) based approaches on popular benchmark datasets. The main objective of this work is to stimulate more effort towards important aspects of the research such as data, experiment design, evaluation methodology and result interpretability, instead of putting the highest weight on the design of increasingly more complex and "fancier" algorithms.

翻译：多变量时间序列（MVTS）异常检测是一个长期且富有挑战性的研究课题，近年来吸引了工业界和学术界的广泛研究投入。然而，对文献的仔细研究让我们意识到：1）该领域虽活跃，但不如计算机视觉（CV）和自然语言处理（NLP）等兄弟机器学习社区组织有序；2）大多数提出的解决方案采用的评估协议要么不恰当，要么存在严重缺陷，明显缺乏科学基础。一种名为\pa协议的流行协议问题尤为严重，以至于随机猜测竟然能够系统性地优于迄今为止开发的所有算法。在本文中，我们使用更稳健的协议评审并评估了多种近期算法，讨论了通常良好的协议在MVTS异常检测背景下可能存在的弱点及缓解方法。我们还对许多工作中观察到的基准数据集、实验设计和评估方法提出了担忧。此外，我们提出了一种基于主成分分析（PCA）的简单但具有挑战性的基线算法，该算法在流行的基准数据集上出人意料地优于许多基于深度学习（DL）的方法。本工作的主要目标是激励研究者更加关注数据、实验设计、评估方法和结果可解释性等重要研究方面，而非将最高权重放在设计日益复杂和“花哨”的算法上。