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Warp Quantification Analysis: A Framework for Path-Based Signal Alignment Metrics

Year: 2026

Type: conference-paper

Abstract: Dynamic time warping (DTW) is widely used to align time series evolving on mismatched timescales, yet most applications reduce alignment to a scalar distance. We introduce warp quantification analysis (WQA), a framework that derives interpretable geometric and structural descriptors from DTW paths. Controlled simulations showed that each metric selectively tracked its intended driver with minimal crosstalk. Applied to large-scale fMRI, WQA revealed distinct network signatures and complementary associations with schizophrenia negative symptom severity, capturing clinically meaningful variability beyond DTW distance. WQA transforms DTW from a single-score method into a family of alignment descriptors, offering a principled and generalizable extension for richer characterization of temporal coupling across domains where nonlinear normalization is essential.

Source: IEEE International Conference on Acoustics Speech and Signal Processing

DOI: 10.1109/icassp55912.2026.11460580

Authors: Sir-Lord Wiafe, Vince D. Calhoun

Institutions: Georgia State University, Center for Translational Research in Neuroimaging and Data Science

Language: English

Topic: Time Series Analysis and Forecasting

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, HORIZON EUROPE Health

OpenAlex ID: W4416256333

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