k-Shape: Efficient and Accurate Clustering of Time Series
John Paparrizos and Luis Gravano
For the paper from the SIGMOD proceedings 10-12 years prior that has had the most impact over the intervening decade. In particular, this paper receives the SIGMOD 2025 test-of-time award for advancing time-series clustering through a shape-based approach grounded in cross-correlation, achieving a distinctive synthesis of high accuracy, computational efficiency, and broad-domain applicability.