<p>A time series is a sequence of numerical data arriving sequentially as a data stream. Typically, data streams’ generating distributions change over time (i.e., concept drifts), potentially degrading predictive model performance. Despite extensive literature on time series forecasting and concept drift handling separately, few studies address both problems together. Recently, Particle Swarm Optimization (PSO) has been applied to dynamic optimization problems and training neural networks for forecasting. Building on these PSO applications, we propose the first approach using dynamic swarm intelligence to address concept drift in time series forecasting. Our approach includes three strategies: (i) using swarm models to detect concept drifts; (ii) managing swarm models to adapt to new concepts; and (iii) utilizing past swarm models for recurrent drifts. Experiments with six synthetic and four real-world datasets demonstrated that the proposed approach was competitive in concept drift detection, achieving the best forecasting errors compared to existing methods. This suggests dynamic swarm intelligence is a promising method for handling concept drift in time series forecasting.</p>