Warp Quantification Analysis: A Framework For Path-based Signal Alignment Metrics
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912844894699520 |
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| author | Wiafe, Sir-Lord Calhoun, Vince D. |
| author_facet | Wiafe, Sir-Lord Calhoun, Vince D. |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_14994 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Warp Quantification Analysis: A Framework For Path-based Signal Alignment Metrics Wiafe, Sir-Lord Calhoun, Vince D. Computational Engineering, Finance, and Science 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. |
| title | Warp Quantification Analysis: A Framework For Path-based Signal Alignment Metrics |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2509.14994 |