用深度强化学习自动学触发函数，提前判断时间序列更准更快
Alert: Learning Trigger Functions for Early Classification of Time Series using Deep-RL
- 用深度强化学习自动学何时触发预测，不依赖人工规则
- 在30个数据集上验证，新方法在准确率和延迟间平衡更好
- 适合工业监控、医疗分诊等需快速决策的场景
时间序列早期分类（ECTS）在工业监测和医疗分诊等领域至关重要，要求快速准确的预测。核心挑战在于触发函数——决定何时做出预测，与分类器本身独立。现有方法多依赖手工规则，数据驱动方法能否超越？本文提出Alert，一种基于深度强化学习的框架，可从任意状态表示中学习触发函数。在30个数据集上的系统性对比显示，状态空间设计显著影响性能。基于此，我们提出Alert+，一种简单有效的方法，在不平衡误分类与指数延迟成本设置下，持续优于传统方法，实现准确率与延迟的更好权衡。Alert与Alert+已开源，支持可复现研究与实际应用。
原文摘要 · Abstract (English)
Early Classification of Time Series (ECTS) is vital in fields like industrial monitoring and medical triage, where quick and accurate predictions are essential. One of the core challenges lies in the trigger function, which decides when to make a prediction, independently of the classifier itself. Most existing methods rely on handcrafted rules, but can data-driven approaches outperform them? This paper introduces Alert, a Deep-RL framework that learns trigger functions from any state representation. Systematic comparisons on 30 datasets show that the design of the state space significantly influences performance. Building on this, we propose Alert+, a simple yet effective variant that consistently outperforms traditional methods in balancing accuracy and delay within an imbalanced misclassification and exponential delay cost setting. Alert and Alert+ are released to support reproducible research and practical applications.
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