解决时间序列迁移学习中的频域偏移问题，提升无源域适应性能。
Temporal-Spectral Alignment with Frequency Adaptation for Source-Free Time-Series Adaptation
- 多尺度建模时序与频谱特征，捕捉源域复杂结构。
- 引入可训练频域调制模块，动态调整目标信号相位和幅度。
- 在多个基准数据集上验证有效，尤其适合信号频谱漂移场景。
时间序列的无源域适应（SFDA）旨在将预训练源模型的知识迁移到未标注的目标域，同时应对信号中固有的特征偏移和时序漂移。尽管现有方法已探索无监督环境下的时序动态，但普遍忽视了时间序列数据中的频谱偏移问题。为此，本文提出一种新方法——时序-频谱对齐与频率自适应（SAFA）。首先，通过联合建模时序依赖性和频谱特性，在多尺度下刻画源域分布；其次，设计一个可训练的频率自适应模块，对目标信号在频域中调节其相位和幅值，使其与源域分布对齐。在多个基准数据集上的大量实验表明，SAFA在性能和鲁棒性方面均显著优于现有方法。
原文摘要 · Abstract (English)
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.
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