[
    {
        "id": "osp-19603",
        "type": "article-journal",
        "title": "Adaptive Spectral-Koopman Dynamics Modeling for Temporal Domain Generalization",
        "author": [
            {
                "family": "Zhang",
                "given": "Tengxue"
            },
            {
                "family": "Ke",
                "given": "Yu"
            },
            {
                "family": "Shu",
                "given": "Yang"
            },
            {
                "family": "Sun",
                "given": "Chenchen"
            },
            {
                "family": "An",
                "given": "Yisheng"
            },
            {
                "family": "Guo",
                "given": "Chenjuan"
            },
            {
                "family": "Yang",
                "given": "Bin"
            }
        ],
        "URL": "https://omanscience.com/en/articles/adaptive-spectral-koopman-dynamics-modeling-for-temporal-domain-generalization",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "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}."
    }
]