Prompt-based Domain Discrimination for Multi-source Time Series Domain Adaptation

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Publication Date: 12/21/2023

Event: https://arxiv.org

Reference: https://arxiv.org/abs/2312.12276

Authors: Junxiang Wang, NEC Laboratories America, Inc.; Guangji Bai, Emory University; Wei Cheng, NEC Laboratories America, Inc.; ZhengZhang Chen, NEC Laboratories America, Inc.; Liang Zhao, Emory University; Haifeng Chen, NEC Laboratories America, Inc.

Abstract: Time series domain adaptation stands as a pivotal and intricate challenge with diverse applications, including but not limited to human activity recognition, sleep stage classification, and machine fault diagnosis. Despite the numerous domain adaptation techniques proposed to tackle this complex problem, their primary focus has been on the common representations of time series data. This concentration might inadvertently lead to the oversight of valuable domain-specific information originating from different source domains. To bridge this gap, we introduce POND, a novel prompt-based deep learning model designed explicitly for multi-source time series domain adaptation. POND is tailored to address significant challenges, notably: 1) The unavailability of a quantitative relationship between meta-data information and time series distributions, and 2) The dearth of exploration into extracting domain specific meta-data information. In this paper, we present an instance-level prompt generator and afidelity loss mechanism to facilitate the faithful learning of meta-data information. Additionally, we propose a domain discrimination technique to discern domain-specific meta-data information from multiple source domains. Our approach involves a simple yet effective meta-learning algorithm to optimize the objective efficiently. Furthermore, we augment the model’s performance by incorporating the Mixture of Expert (MoE) technique. The efficacy and robustness of our proposed POND model are extensively validated through experiments across 50 scenarios encompassing five datasets, which demonstrates that our proposed POND model outperforms the state-of the-art methods by up to 66% on the F1-score.

Publication Link: https://doi.org/10.48550/arXiv.2312.12276