From Diversity to Uniformity: Cross-modal Time Series Modeling with Dependent Channel Grouping

Abstract

Emerging foundation models have spurred growing interest in task-unspecific time series modeling, which can accommodate data from diverse domains and support various tasks. However, most existing methods still suffer from poor adaptability and generalization across cross-domain time series with varying feature dimensionalities. This study achieves unified time series modeling to transcend task and data boundaries, inspired by the recent advances in visual foundation models. We propose a universal cross-modal Time series modeling method named TimeIG, featuring Tailored Temporal Imaging and Dependent Channel Grouping. Specifically, TimeIG adopts the novel dependent channel grouping method to project heterogeneous time series into hierarchical spaces to identify representative features and learn inter-dependencies across variables. Then, a tailored temporal imaging method is proposed to generate carefully hand-crafted images from time series, facilitating complementary temporal-spatial feature extraction. Finally, a dual-branch module is designed to simultaneously model the sequential and visual data, enabling cross-modal alignment via the proposed Cross-Modal Attention and Dynamic Weighted-Averaging. Extensive experiments on real-world cross-domain time series datasets show that TimeIG consistently outperforms existing SOTA methods. The code of TimeIG is publicly available at https://github.com/missCmj/TimeIG.