Bridging Resolutions for Domain-Invariant Representation Learning in Time Series

Unsupervised time-series domain adaptation (TSDA) aims to mitigate distribution shifts between labeled source domains and unlabeled target domains. Existing methods typically rely on two extreme views of time series, namely fine-grained temporal representations and coarse global frequency representations, while overlooking crucial intermediate-resolution structures for progressive domain-invariant learning. To address this limitation, we investigate the previously unexplored intermediate-resolution hierarchy induced by the Stationary Wavelet Transform and reveal two key properties: different resolutions exhibit distinct transferability and discriminability characteristics under domain shift, while different resolutions exhibit distinct category-level decision strengths. Based on these properties, we propose **PRISM**, a TSDA framework that bridges progressive representations across resolutions for domain-invariant learning. PRISM introduces Cross-Resolution Knowledge Transfer to bridge multi-resolution representations by progressively exchanging complementary knowledge across resolutions, and Committee-based Consensus Training to bridge the cross-resolution decision hierarchy, enabling more consistent domain-invariant decisions. Extensive experiments on six datasets demonstrate that PRISM consistently outperforms existing methods, achieving an average accuracy improvement of 4.21% in cross-domain settings. Code is available at the anonymous link: https://anonymous.4open.science/r/PRISM-E7BC/.