AI-Enhanced Distributed Time Series Modeling: Incremental Learning for Evolving Streaming Data

Abstract

This study proposes an incremental learning framework for time series modeling in distributed systems to address the challenges of dynamic modeling and continuous learning in real-time streaming environments. The framework integrates a distributed parallel architecture with an incremental optimization mechanism to achieve online feature extraction and adaptive parameter updates for non-stationary streaming data. The system consists of four layers: data stream sampling, distributed time series modeling, incremental updating, and global coordination. It maintains model consistency and convergence stability under multi-node asynchronous communication conditions. By introducing drift detection and weight constraint mechanisms at the node level, the model dynamically adjusts the learning rate according to data distribution changes, effectively mitigating concept drift and forgetting problems. To verify the effectiveness of the proposed algorithm, sensitivity experiments were conducted to analyze the impact of learning rate, optimizer, weight decay coefficient, and communication delay on model performance. The results show that the proposed distributed incremental learning method achieves low prediction error and high robustness under various complex streaming data conditions, maintaining stable convergence as data continuously evolves. This research provides a scalable, high-precision, and low-latency solution for real-time streaming data modeling and holds important significance for dynamic optimization and continuous learning in distributed intelligent systems.