Domain-Adaptive Continual Meta-Learning for Modeling Dynamical Systems: An Application in Environmental Ecosystems

Environmental ecosystems exhibit complex and evolving dynamics over time, making the modeling of non-stationary processes critically important. However, traditional methods often rely on static models trained on entire datasets, failing to capture the non-stationary and drastically fluctuating characteristics. Dynamically adjusting models to evolving data is challenging, as they can easily either lag behind new trends or overfit newly received data. To address these challenges, we propose Domain-Adaptive Continual Meta-Learning (DACM) method, aiming to automatically detect distribution shifts and adapt to newly emergent domains. In particular, while DACM continuously explores the sequential temporal data, it also exploits historical data that are similar in distribution to the current observations. By striking a balance between temporal exploration and distributional exploitation, DACM quickly adjusts the model to stay up-to-date with new trends while maintaining generalization ability to data with similar distributions. We demonstrate the effectiveness of DACM on a real-world water temperature prediction dataset, where it outperforms diverse baseline models and shows strong adaptability and predictive performance in non-stationary environments.