[2603.21828] CoRA: Boosting Time Series Foundation Models for Multivariate Forecasting through Correlation-aware Adapter

Computer Science > Machine Learning arXiv:2603.21828 (cs) [Submitted on 23 Mar 2026] Title:CoRA: Boosting Time Series Foundation Models for Multivariate Forecasting through Correlation-aware Adapter Authors:Hanyin Cheng, Xingjian Wu, Yang Shu, Zhongwen Rao, Lujia Pan, Bin Yang, Chenjuan Guo View a PDF of the paper titled CoRA: Boosting Time Series Foundation Models for Multivariate Forecasting through Correlation-aware Adapter, by Hanyin Cheng and 6 other authors View PDF HTML (experimental) Abstract:Most existing Time Series Foundation Models (TSFMs) use channel independent modeling and focus on capturing and generalizing temporal dependencies, while neglecting the correlations among channels or overlooking the different aspects of correlations. However, these correlations play a vital role in Multivariate time series forecasting. To address this, we propose a CoRrelation-aware Adapter (CoRA), a lightweight plug-and-play method that requires only fine-tuning with TSFMs and is able to capture different types of correlations, so as to improve forecast performance. Specifically, to reduce complexity, we innovatively decompose the correlation matrix into low-rank Time-Varying and Time-Invariant components. For the Time-Varying component, we further design learnable polynomials to learn dynamic correlations by capturing trends or periodic patterns. To learn positive and negative correlations that appear only among some channels, we introduce a novel dual contrastive learning m...