Adaptive law-based feature representation for time series classification.

ABSTRACT: Time series classification (TSC) underpins applications across finance, healthcare, and environmental monitoring, yet real-world series often contain noise, local misalignment, and multiscale patterns. We introduce adaptive law-based transformation (ALT), a multiscale generalization of the earlier linear law-based transformation (LLT). ALT scans each series with variable-length, shifted windows, constructs symmetric delay embeddings, and extracts eigenvectors associated with the eigenvalue of minimal magnitude ("shapelet laws") that capture locally stable patterns. These laws are assembled into class-specific dictionaries, and new series are projected onto them to yield compact, transparent features that enhance linear separability while remaining compatible with standard classifiers. On t

SUBMITTER: Kurbucz MT 

PROVIDER: S-EPMC12647884 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature