An Incremental Learning Framework for Industrial Time Series Prediction With Sample‐Importance‐Aware Replay and Performance‐Driven Iterative Ensemble
Wei Zeng, Yiming Zhang, Guodong Yi, Shuyou Zhang, Zili Wang, Yangjian Ji, Hongchao Wang · Concurrency and Computation Practice and Experience · 2025
ABSTRACT Production data, a critical component of industrial datasets derived from production processes, is widely used to train data‐driven models for forecasting and managing industrial processes. However, shifts in data distribution, caused by changes in production environments, operating conditions, and equipment states, disrupt the consistency between the training and deployment, and lead to catastrophic forgetting and a significant deterioration in both model prediction accuracy and stability. Although existing incremental learning methods have improved adaptability and mitigated forgetting, challenges remain in balancing knowledge retention with dynamic sample selection and ensemble optimization, particularly in complex industrial settings. To address these challenges, this paper proposes an incremental learning framework that includes two key strategies: sample‐importance‐aware buffer update and elastic weight consolidation (EWC) based learner construction for knowledge retention, and performance‐driven iterative strong learner construction with multi‐objective weight optimization. The buffer update dynamically adjusts capacity according to training loss fluctuations, selects high‐information samples guided by loss rates and uncertainty estimation, and maintains diversity through K‐means clustering. EWC consolidates previously acquired knowledge to mitigate forgetting during weak learner training. The ensemble construction evaluates individual learner performance comprehensively and iteratively adjusts model weights using a multi‐objective optimization method, balancing prediction accuracy, stability, and uncertainty. Experimental results on multiple publicly available industrial datasets, complemented by an external validation on a financial dataset, demonstrate that the proposed method outperforms several representative approaches in both accuracy and stability of prediction.