Description

This Master Prompt addresses forecasting systems operating in dynamic environments where temporal patterns, data distributions, external conditions, and underlying relationships evolve over time. It examines nonstationarity, concept drift, structural breaks, regime changes, distribution shifts, evolving seasonality, forecast revision behaviour, and time-varying dependencies. The theme encompasses rolling-window estimation, expanding-window learning, online forecasting, incremental learning, continual learning, adaptive ensembles, dynamic model weighting, transfer learning, domain adaptation, meta-learning, forecast correction, residual recalibration, and sequential Bayesian updating. It investigates drift detection, change-point identification, adaptive hyperparameter selection, knowledge retention, catastrophic forgetting, transferability across locations and operating conditions, and real-time model monitoring. Emphasis is placed on designing robust forecasting systems capable of maintaining accuracy, calibration, computational efficiency, and operational reliability under evolving conditions, incomplete observations, emerging data sources, and changing system configurations.