Symmetry-Aware Continual Learning for Dynamic Dimensional Multivariate Time Series Forecasting: Integrating Redundancy Clustering and Multi-LoRA Adapters

Continual learning of multivariate time series (MTS) forecasting is critical for process industries where working condition drift is frequent due to the variation in the feed properties and other factors. However, existing continual learning methods struggle with dynamic input dimension changes, and the lack of symmetry-aware feature and dimension regulation further exacerbates the interference of irrelevant variables and dimensional inconsistency. To overcome this problem, G-MLoRA, a continual learning method based on dynamic redundancy clustering and multiple low-rank adapters, is proposed in this paper. This method can effectively enhance the network’s capability for prediction of multivariate time series under dynamic input dimensions. First, it groups MTS via Wasserstein distance-K-means clustering to reduce irrelevant variable interference. Second, each group is assigned to an exclusive LoRA adapter, with pre-trained backbone weights frozen during fine-tuning to lower complexity and mitigate catastrophic forgetting. Third, mini-batch gradient accumulation enables reuse of inconsistent-dimensional historical knowledge. Extensive experiments on two real grinding classification datasets show G-MLoRA outperforms baselines in new/historical knowledge compatibility, especially under dynamic dimensions.