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

Two distinct technical guides are presented. The first provides a comprehensive OpenTelemetry setup for AI agents and Power BI observability, detailing instrumentation for Python and Node.js to trace LLM and tool-call operations. The second discusses Early Classification of Time Series in non-stationary environments, challenging separable designs. It introduces DQeND, a Reinforcement Learning architecture that jointly learns representation and classification to improve adaptability under drift, offering a systematic comparison against traditional separable approaches in evolving scenarios.