构建时间序列通用域适应基准，评估模型跨域泛化能力
Universal Domain Adaptation Benchmark for Time Series Data Representation
- 设计统一评估协议，测试不同时间序列模型在域迁移下的表现
- 发现骨干网络选择对通用域适应性能影响显著，决定模型鲁棒性
- 为未来时间序列域适应研究提供可扩展的基准框架，适合工业部署
深度学习模型在时间序列（TS）数据异常检测中取得显著进展，主要得益于其强大的表征能力。然而，由于时间序列数据固有的变异性，这些模型在泛化和鲁棒性方面仍面临挑战。为此，常用方法是进行无监督域适应，特别是通用域适应（UniDA），以应对域偏移和新类别出现的问题。尽管在计算机视觉领域已有广泛研究，但针对时间序列数据的UniDA仍处于探索阶段。本文全面实现了最先进的时间序列骨干网络在UniDA框架下的对比与评估，提出可靠的评测协议，以检验其在不同域间的鲁棒性和泛化能力。目标是为从业者提供一个易于扩展的框架，可集成未来在UniDA和时间序列架构方面的进步。实验结果揭示了骨干网络选择对UniDA性能的关键影响，并支持在多种数据集和架构上的鲁棒性分析。
原文摘要 · Abstract (English)
Deep learning models have significantly improved the ability to detect novelties in time series (TS) data. This success is attributed to their strong representation capabilities. However, due to the inherent variability in TS data, these models often struggle with generalization and robustness. To address this, a common approach is to perform Unsupervised Domain Adaptation, particularly Universal Domain Adaptation (UniDA), to handle domain shifts and emerging novel classes. While extensively studied in computer vision, UniDA remains underexplored for TS data. This work provides a comprehensive implementation and comparison of state-of-the-art TS backbones in a UniDA framework. We propose a reliable protocol to evaluate their robustness and generalization across different domains. The goal is to provide practitioners with a framework that can be easily extended to incorporate future advancements in UniDA and TS architectures. Our results highlight the critical influence of backbone selection in UniDA performance and enable a robustness analysis across various datasets and architectures.
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