自适应谱-Koopman动力学建模用于时间域泛化
Adaptive Spectral-Koopman Dynamics Modeling for Temporal Domain Generalization
- East China Normal University（华东师范大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对时间域泛化中噪声过拟合和复杂历史建模问题，提出谱正则化Koopman动力学与目标条件注意力机制，在八个基准上取得最优性能。
AI中文摘要：
时间域泛化（Temporal Domain Generalization, TDG）已被提出以应对随时间发生分布偏移的真实世界流数据。然而，现有方法要么容易在数据空间中对特定于领域的噪声过拟合，要么在参数空间中变得过于复杂且可解释性较差。为弥合这些差距，我们提出了AdaSpecK，一种具有自适应上下文提取的谱-Koopman框架，用于TDG。为缓解对不规则采样领域的噪声拟合，我们引入了谱正则化Koopman动力学建模，该方法在潜在空间中应用谱感知滤波以提取去噪的低频轨迹，并学习一个Koopman算子以在线性化空间中建模系统动力学。为在非平稳性下建模复杂的历史环境，我们设计了一种上下文感知的异质模式提取机制。具体而言，我们采用一个目标条件注意力模块来关注不同的过去窗口，生成动态的、目标特定的历史摘要。通过从当前演化模式构建环境签名，我们的模型通过一个学习到的路由器自适应地感知过去上下文中哪些方面对未来的预测最有信息量。在八个多样化的分类和回归基准上的广泛实验表明，AdaSpecK达到了最先进的性能。代码和数据集可在https://这个URL获取。
英文摘要：
Temporal Domain Generalization (TDG) has emerged to address real-world streaming data with distribution shifts over time. However, existing methods are either prone to overfitting to domain-specific noise in the data space or become overly complex and less interpretable in the parameter space. To bridge these gaps, we propose \textbf{AdaSpecK}, a spectral-Koopman framework with adaptive context extraction for TDG. To mitigate noise fitting to irregularly sampled domains, we introduce spectral-regularized Koopman dynamics modeling, which applies spectral-aware filtering in the latent space to extract denoised low-frequency trajectories and learn a Koopman operator to model the system dynamics in a linearized space. To model complex historical environments under non-stationarity, we design a context-informed heterogeneous pattern extraction mechanism. Specifically, we employ a target-conditioned attention module to attend to distinct past windows, producing a dynamic, target-specific historical summary. By constructing an environmental signature from the current evolutionary pattern, our model adaptively perceives which aspects of the past context are most informative for future prediction via a learned router. Extensive experiments on eight diverse classification and regression benchmarks demonstrate that AdaSpecK achieves state-of-the-art performance. The code and datasets are available at \href{}{https://anonymous.4open.science/r/Ada-Spec-K}.
