End-to-end Early Classification of Time Series in Non-Stationary Environments

2026-08-20

AI Summary

研究在非平稳环境下时间序列的早期分类问题，通过强化学习提出DQeND模型联合学习表示、分类和触发决策，优于传统分离设计方法。

Abstract

Early Classification of Time Series (ECTS) requires making accurate decisions as early as possible in inherently online and evolving environments. Yet, most existing methods assume stationarity and rely on separable designs, where classification and triggering are optimized independently, an assumption that fundamentally limits their adaptability under drift. In this work, we challenge this paradigm and study ECTS under non-stationary conditions. We provide the first systematic comparison between separable and end-to-end approaches across controlled drifting scenarios. Building on Reinforcement Learning, we introduce DQeND, a unified architecture that jointly learns representation, classification, and triggering decisions, while remaining directly comparable to state-of-the-art separable baselines. Across a wide range of drifts, DQeND demonstrates strong robustness across various non-stationary scenarios, consistently outperforming separable baselines. An ablation study further highlights that jointly updating representation and decision modules is critical to these gains. Overall, our results indicate that end-to-end learning can offer improved adaptation capabilities for ECTS in dynamic environments, and motivate further investigation of alternatives to separable designs.

Problem

• Early Classification of Time Series
• non-stationary environments
• drift

Innovation

• Reinforcement Learning
• End-to-end Learning
• Non-stationary Environments
• Early Classification of Time Series (ECTS)
• DQeND