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，旨在识别异常模式或数据点，与许多实际问题密切相关，尤其在金融、制造业、网络安全等应用领域。尽管异常检测已在多个领域得到广泛研究，但在异常发生前预测未来异常仍属未探索领域。本文提出一种新型异常检测方法，称为**异常前兆**（Precursor-of-Anomaly，PoA）检测。与传统异常检测专注于判定给定时间序列观测值是否为异常不同，PoA检测旨在异常发生前检测未来异常。为同时解决两类问题，我们提出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个基线方法和3个数据集（涵盖规则与非规则时间序列）进行实验，结果表明所提方法在几乎所有场景下均优于基线方法。消融研究亦表明，多任务训练方法显著提升了异常检测与PoA检测的整体性能。