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 Precursor-of-Anomaly (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：异常检测是一个旨在识别意外模式或数据点的重要领域，它与许多现实世界问题密切相关，尤其是在金融、制造、网络安全等应用中。虽然异常检测已在多个领域得到广泛研究，但检测未来异常发生前的迹象仍是一个尚未探索的领域。在本文中，我们提出了一种新型的异常检测方法，称为异常前兆（Precursor-of-Anomaly, PoA）检测。与传统的仅判断给定时间序列观测值是否为异常的检测方式不同，PoA检测旨在预测异常发生前的征兆。为同时解决这两个问题，我们提出了一种基于神经受控微分方程的神经网络及其多任务学习算法。我们使用17个基准模型和3个数据集（包括规则与不规则时间序列）开展实验，结果表明所提方法在几乎所有场景下均优于基准模型。消融研究也表明，多任务训练方法显著提升了异常检测与PoA检测的综合性能。