  In real-world applications, machine learning models often become obsolete due
to shifts in the joint distribution arising from underlying temporal trends, a
phenomenon known as the "concept drift". Existing works propose model-specific
strategies to achieve temporal generalization in the near-future domain.
However, the diverse characteristics of real-world datasets necessitate
customized prediction model architectures. To this end, there is an urgent
demand for a model-agnostic temporal domain generalization approach that
maintains generality across diverse data modalities and architectures. In this
work, we aim to address the concept drift problem from a data-centric
perspective to bypass considering the interaction between data and model.
Developing such a framework presents non-trivial challenges: (i) existing
generative models struggle to generate out-of-distribution future data, and
(ii) precisely capturing the temporal trends of joint distribution along
chronological source domains is computationally infeasible. To tackle the
challenges, we propose the COncept Drift simulAtor (CODA) framework
incorporating a predicted feature correlation matrix to simulate future data
for model training. Specifically, CODA leverages feature correlations to
represent data characteristics at specific time points, thereby circumventing
the daunting computational costs. Experimental results demonstrate that using
CODA-generated data as training input effectively achieves temporal domain
generalization across different model architectures.
