Event detection in time series is a challenging task due to the prevalence of imbalanced datasets, rare events, and time interval-defined events. Traditional supervised deep learning methods primarily employ binary classification, where each time step is assigned a binary label indicating the presence or absence of an event. However, these methods struggle to handle these specific scenarios effectively. To address these limitations, we propose a novel supervised regression-based deep learning approach that offers several advantages over classification-based methods. Our approach, with a limited number of parameters, can effectively handle various types of events within a unified framework, including rare events and imbalanced datasets. We provide theoretical justifications for its universality and precision and demonstrate its superior performance across diverse domains, particularly for rare events and imbalanced datasets.

翻译：时间序列中的事件检测是一项具有挑战性的任务，原因在于数据集中普遍存在不平衡、罕见事件以及由时间区间定义的事件。传统的监督式深度学习方法主要采用二元分类，即为每个时间步分配一个二元标签，以指示事件存在与否。然而，这些方法难以有效处理上述特定场景。为解决这些局限，我们提出了一种新颖的基于回归的监督式深度学习方法，该方法相对于基于分类的方法具有若干优势。我们的方法参数数量有限，能够在统一框架内有效处理多种类型的事件，包括罕见事件和不平衡数据集。我们为其通用性和精确性提供了理论依据，并展示了该方法在多个领域（尤其是针对罕见事件和不平衡数据集）的优越性能。