TSFA: A Two-Stage Feature Alignment Method for Unsupervised Open-Set Domain Adaptation in Time-Series Classification

The unsupervised open-set domain adaptation (UOSDA) in computer vision is widely studied. However, designing UOSDA algorithms for time series remains challenging due to the complex nonstationary property of data and distribution shifts across different operating conditions, which heighten the risk of negative transfer. To address this, a novel two-stage feature alignment (TSFA) method for UOSDA in time-series classification is proposed. First, a time-frequency feature extractor is designed to ef