Evidentially Calibrated Source-Free Time-Series Domain Adaptation with Temporal Imputation

TL;DR

This paper introduces MAPU, a novel method for source-free time series domain adaptation that emphasizes temporal consistency through imputation, and E-MAPU, which improves calibration with evidential uncertainty estimation, achieving significant performance gains.

Contribution

It presents the first approach explicitly addressing temporal consistency in time series SFDA and integrates evidential uncertainty estimation for better calibration and domain alignment.

Findings

MAPU outperforms existing methods on five real-world datasets.

E-MAPU improves model calibration and domain adaptation performance.

Temporal imputation enhances the capture of time series dynamics in SFDA.

Abstract

Source-free domain adaptation (SFDA) aims to adapt a model pre-trained on a labeled source domain to an unlabeled target domain without access to source data, preserving the source domain's privacy. While SFDA is prevalent in computer vision, it remains largely unexplored in time series analysis. Existing SFDA methods, designed for visual data, struggle to capture the inherent temporal dynamics of time series, hindering adaptation performance. This paper proposes MAsk And imPUte (MAPU), a novel and effective approach for time series SFDA. MAPU addresses the critical challenge of temporal consistency by introducing a novel temporal imputation task. This task involves randomly masking time series signals and leveraging a dedicated temporal imputer to recover the original signal within the learned embedding space, bypassing the complexities of noisy raw data. Notably, MAPU is the first method to explicitly address temporal consistency in the context of time series SFDA. Additionally, it offers seamless integration with existing SFDA methods, providing greater flexibility. We further introduce E-MAPU, which incorporates evidential uncertainty estimation to address the overconfidence issue inherent in softmax predictions. To achieve that, we leverage evidential deep learning to obtain a better-calibrated pre-trained model and adapt the target encoder to map out-of-support target samples to a new feature representation closer to the source domain's support. This fosters better alignment, ultimately enhancing adaptation performance. Extensive experiments on five real-world time series datasets demonstrate that both MAPU and E-MAPU achieve significant performance gains compared to existing methods. These results highlight the effectiveness of our proposed approaches for tackling various time series domain adaptation problems.