Temporal-Spectral Alignment with Frequency Adaptation for Source-Free Time-Series Adaptation

2026-06-22

AI Summary

This work addresses the challenges of feature shift, temporal drift, and spectral discrepancy in source-free domain adaptation for time series. To tackle these issues without access to source-domain data, the authors propose a time–frequency joint alignment approach that explicitly corrects spectral shifts—a first in the source-free setting. By modeling the source domain’s temporal dependencies and spectral characteristics at multiple scales, they design a trainable frequency-domain adaptation module that modulates both phase and amplitude of target-domain signals to achieve distribution alignment. Extensive experiments demonstrate that the proposed method significantly outperforms existing source-free time series adaptation techniques across multiple benchmark datasets, confirming its effectiveness and robustness.

Abstract

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.

Problem

• source-free domain adaptation
• time-series data
• spectral shift
• temporal drift
• feature shift

Innovation

• source-free domain adaptation
• time-series adaptation
• spectral alignment
• frequency adaptation
• temporal-spectral modeling