Warping and Matching Subsequences Between Time Series
This work addresses the need for clearer structural understanding in time series comparison for researchers and practitioners, though it is incremental as it builds on existing elastic distance measures.
The paper tackled the problem of lacking qualitative comparison in time series analysis by proposing a technique that simplifies warping paths to highlight and visualize key transformations like shift and compression, enhancing interpretability in tasks such as clustering and classification.
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.