Anomaly detection is an important field that aims to identify unexpected patterns or data points, and it is closely related to many real-world problems, particularly to applications in finance, manufacturing, cyber security, and so on. While anomaly detection has been studied extensively in various fields, detecting future anomalies before they occur remains an unexplored territory. In this paper, we present a novel type of anomaly detection, called \emph{\textbf{P}recursor-of-\textbf{A}nomaly} (PoA) detection. Unlike conventional anomaly detection, which focuses on determining whether a given time series observation is an anomaly or not, PoA detection aims to detect future anomalies before they happen. To solve both problems at the same time, we present a neural controlled differential equation-based neural network and its multi-task learning algorithm. We conduct experiments using 17 baselines and 3 datasets, including regular and irregular time series, and demonstrate that our presented method outperforms the baselines in almost all cases. Our ablation studies also indicate that the multitasking training method significantly enhances the overall performance for both anomaly and PoA detection.

翻译：异常检测是一个重要的研究领域，旨在识别意外的模式或数据点，它与许多实际问题密切相关，特别是在金融、制造、网络安全等应用中。尽管异常检测已在各个领域得到广泛研究，但检测未来尚未发生的异常仍是一片未开发的领域。在本文中，我们提出了一种新型异常检测，称为**异常前兆（PoA）检测**。与专注于判断给定时间序列观测是否为异常的常规异常检测不同，PoA检测旨在检测未来异常发生前的征兆。为了同时解决这两个问题，我们提出了一种基于神经控制微分方程的神经网络及其多任务学习算法。我们使用17个基线和3个数据集（包括规则和不规则时间序列）进行了实验，结果表明，我们提出的方法在几乎所有情况下都优于基线方法。我们的消融研究也表明，多任务训练方法显著提升了异常检测和PoA检测的整体性能。