End-to-end Early Classification of Time Series in Non-Stationary Environments
DQeND is introduced, a unified architecture that jointly learns representation, classification, and triggering decisions, while remaining directly comparable to state-of-the-art separable baselines, and indicates that end-to-end learning can offer improved adaptation capabilities for ECTS in dynamic environments.