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 a fidelity 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.

翻译：时间序列域适应是一个关键且复杂的问题，在人类活动识别、睡眠阶段分类以及机器故障诊断等多种应用中具有重要意义。尽管已有多种域适应技术被提出以解决这一复杂问题，但它们主要关注于时间序列数据的共同表示。这种关注可能无意中导致源自不同源域的宝贵域特定信息的忽视。为弥补这一不足，我们提出了POND，一种专为多源时间序列域适应设计的新型基于提示的深度学习模型。POND旨在解决两个重要挑战：1）元数据信息与时间序列分布之间定量关系的不可用性，以及2）提取域特定元数据信息的探索不足。本文中，我们提出了一种实例级提示生成器和保真度损失机制，以促进元数据信息的忠实学习。此外，我们设计了一种域判别技术，用于从多个源域中识别域特定元数据信息。我们的方法包含一个简单而有效的元学习算法，以高效优化目标。进一步地，我们通过引入混合专家技术来增强模型性能。通过在涵盖五个数据集的50种场景下进行广泛实验，我们提出的POND模型的有效性和鲁棒性得到了充分验证，结果表明，我们的POND模型在F1分数上相较于最先进方法提升了高达66%。