[2603.02756] Rethinking Time Series Domain Generalization via Structure-Stratified Calibration

Computer Science > Machine Learning arXiv:2603.02756 (cs) [Submitted on 3 Mar 2026] Title:Rethinking Time Series Domain Generalization via Structure-Stratified Calibration Authors:Jinyang Li, Shuhao Mei, Xiaoyu Xiao, Shuhang Li, Ruoxi Yun, Jinbo Sun View a PDF of the paper titled Rethinking Time Series Domain Generalization via Structure-Stratified Calibration, by Jinyang Li and 4 other authors View PDF HTML (experimental) Abstract:For time series arising from latent dynamical systems, existing cross-domain generalization methods commonly assume that samples are comparably meaningful within a shared representation space. In real-world settings, however, different datasets often originate from structurally heterogeneous families of dynamical systems, leading to fundamentally distinct feature distributions. Under such circumstances, performing global alignment while neglecting structural differences is highly prone to establishing spurious correspondences and inducing negative transfer. From the new perspective of cross-domain structural correspondence failure, we revisit this problem and propose a structurally stratified calibration framework. This approach explicitly distinguishes structurally consistent samples and performs amplitude calibration exclusively within structurally compatible sample clusters, thereby effectively alleviating generalization failures caused by structural incompatibility. Notably, the proposed framework achieves substantial performance improvement...