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

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 controlled empirical comparison between separable and end-to-end approaches under multiple drift regimes. 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. 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.