提出新方法可视化时间序列子段间的变形关系，提升可解释性。
Warping and Matching Subsequences Between Time Series
- 将扭曲路径简化，突出显示子段的位移、压缩与幅度差异
- 量化并可视化两个时间序列间的关键变换模式
- 适合需要理解时序对齐机制的研究者和工程师
时间序列比较在聚类和分类等任务中至关重要。虽然允许扭曲的弹性距离度量提供了稳健的定量比较，但缺乏在此基础上的定性分析。传统可视化聚焦点对点对齐，无法传达子段层面的结构关系，难以揭示一个时间序列相对于另一个在何处发生偏移、加速或减速。为此，我们提出一种新方法，将扭曲路径简化，以突出、量化并可视化关键变换（如位移、压缩、幅度差异）。该方法通过更清晰地呈现时间序列间子段的匹配关系，显著提升了时间序列比较的可解释性。
原文摘要 · Abstract (English)
Comparing time series is essential in various tasks such as clustering and classification. While elastic distance measures that allow warping provide a robust quantitative comparison, a qualitative comparison on top of them is missing. Traditional visualizations focus on point-to-point alignment and do not convey the broader structural relationships at the level of subsequences. This limitation makes it difficult to understand how and where one time series shifts, speeds up or slows down with respect to another. To address this, we propose a novel technique that simplifies the warping path to highlight, quantify and visualize key transformations (shift, compression, difference in amplitude). By offering a clearer representation of how subsequences match between time series, our method enhances interpretability in time series comparison.
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