Ultralow-Dimensionality Reduction for Identifying Critical Transitions by Spatial-Temporal PCA.

ABSTRACT: Discovering dominant patterns and exploring dynamic behaviors especially critical state transitions and tipping points in high-dimensional time-series data are challenging tasks in study of real-world complex systems, which demand interpretable data representations to facilitate comprehension of both spatial and temporal information within the original data space. This study proposes a general and analytical ultralow-dimensionality reduction method for dynamical systems named spatial-temporal principal component analysis (stPCA) to fully represent the dynamics of a high-dimensional time-series by only a single latent variable without distortion, which transforms high-dimensional spatial information into one-dimensional temporal information based on nonlinear delay-embedding theory. The dyn

SUBMITTER: Chen P 

PROVIDER: S-EPMC12120726 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature