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.

翻译：动态时间扭曲（DTW）被广泛用于对齐时间尺度不匹配的时间序列，然而大多数应用将对齐简化为一个标量距离。我们提出了扭曲量化分析（WQA），这是一个从DTW路径中提取可解释的几何与结构描述符的框架。受控仿真表明，每个度量指标均能选择性地追踪其预期驱动因素，且串扰最小。应用于大规模功能磁共振成像（fMRI）时，WQA揭示了不同的网络特征，以及与精神分裂症阴性症状严重程度的互补性关联，捕捉到了超越DTW距离的、具有临床意义的变异性。WQA将DTW从单一评分方法转变为一个对齐描述符家族，为非线性归一化至关重要的各领域中的时间耦合提供了更丰富的表征，这是一种原则性且可推广的扩展。