Abstract
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.
Abstract (translated)
时间序列的比较在诸如聚类和分类等各种任务中至关重要。虽然弹性距离度量（允许扭曲）提供了稳健的数量化比较，但在此基础上进行定性比较的方法却缺失了。传统的可视化方法侧重于点对点的对齐，并不能传达子序列层面的更广泛的结构关系。这种局限使得难以理解一个时间序列相对于另一个时间序列是如何移动、加速或减速的。 为了解决这个问题，我们提出了一种新的技术，该技术通过简化扭曲路径来突出显示、量化和可视化关键转换（如位移、压缩以及幅度差异），从而帮助更好地理解这些现象。通过提供子序列在不同时间序列之间如何匹配的更清晰表示，我们的方法增强了时间序列比较的可解释性。
URL
https://arxiv.org/abs/2506.15452
