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Abstract

The exponential growth of spatio-temporal data across various domains—such as climate modeling, transportation systems, and biomedical monitoring—has necessitated the development of efficient dimensionality reduction techniques. Traditional methods like Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) have been instrumental in reducing data complexity; however, they often fall short in preserving the intrinsic temporal and spatial dependencies inherent in such datasets. Recent advancements have introduced innovative approaches, including spatio-temporal PCA, neural implicit models, and mesh-agnostic frameworks, which aim to retain the dynamic structures of the original data while achieving significant dimensionality reduction. This paper provides a comprehensive review of these contemporary methodologies, evaluates their efficacy in various application contexts, and discusses their potential in facilitating real-time data analysis and decision-making processes.