Temporal-Spectral Alignment with Frequency Adaptation for Source-Free Time-Series Adaptation
For practitioners deploying time-series models on unlabeled target domains without source data, SAFA provides a robust adaptation method that handles both temporal and spectral shifts.
SAFA addresses spectral shifts overlooked by prior source-free time-series domain adaptation methods, achieving state-of-the-art performance across multiple benchmarks with up to 5% accuracy improvement over baselines.
The goal of source-free domain adaptation (SFDA) for time-series data is to transfer knowledge from a pre-trained source model to an unlabeled target domain without requiring access to source data, while addressing feature shift and temporal drift inherent in the signals. Although existing approaches have explored temporal dynamics in unsupervised source-free adaptation, they largely overlook spectral shifts in time-series data. Towards this end, we propose a novel approach termed temporal-Spectral Alignment with Frequency Adaptation (SAFA) for source-free time-series domain adaptation. Specifically, we first model the source domain at multiple scales by jointly capturing temporal dependencies and spectral characteristics. To adapt time-series data in the target domain, we introduce a trainable frequency adaptation module that modulates the phase and amplitude of target signals in the frequency domain to align them with the source distribution. Extensive experiments on multiple benchmark datasets demonstrate the efficacy and robustness of SAFA.