CPDA: Class-Conditional Path Distribution Alignment for Unsupervised Time-Series Domain Adaptation

2026-08-10

AI Summary

This work addresses distribution shifts in unsupervised time series domain adaptation caused by variations across users, sensors, or environments. The authors propose a non-adversarial framework that aligns class-conditional path distributions between source and target domains in a latent space, leveraging both ground-truth labels and soft pseudo-labels to preserve class semantics during alignment. The key innovation lies in the first-time introduction of class-conditional path distribution alignment, enabled by a composite kernel function integrating semantic features, temporal structure, frequency-domain information, and low-rank path signatures. Theoretical risk bounds are provided to support the approach. Using this signature-spectral kernel as a discrepancy measure with CNN, ResNet18, and TCN backbones, the method significantly outperforms 30 existing techniques—including those based on discrepancy minimization, adversarial learning, and pseudo-labeling—across 13 benchmarks, demonstrating robust and state-of-the-art cross-domain classification performance.

Abstract

Unsupervised time-series domain adaptation (DA) addresses the challenge of transferring a classifier from a labeled source domain to an unlabeled target domain under distribution shifts induced by different users, sensors, devices, acquisition conditions, or temporal dynamics. Existing methods typically mitigate this shift by aligning marginal feature distributions through adversarial training, optimal transport, or moment-based discrepancies. In this paper, we propose Class-Conditional Path Distribution Alignment (CPDA), a non-adversarial discrepancy-based framework that aligns source and target class-conditional latent path distributions rather than only global feature marginals. CPDA introduces a composite signature-spectral kernel that jointly captures pooled semantic features, temporal path structure, frequency-domain information, and low-rank path-signature dynamics, while using source labels and target soft pseudo-labels to perform class-preserving alignment. We further provide a theoretical analysis showing that CPDA defines a valid kernel discrepancy, admits existing moment-matching methods as restricted cases, and yields a class-conditional target-risk bound. Extensive experiments with CNN, ResNet18, and TCN backbones on 13 different time-series DA benchmarks demonstrate the effectiveness of CPDA against 30 discrepancy, adversarial, and pseudo-labeling baselines.

Problem

• unsupervised time-series domain adaptation
• distribution shift
• class-conditional alignment
• latent path distribution
• non-adversarial discrepancy

Innovation

• class-conditional alignment
• path distribution
• signature-spectral kernel
• unsupervised domain adaptation
• time-series