TSB-SEG: A Systematic Time-Series Segmentation Benchmark

Félix Chavelli, Arik Ermshaus, Fan Yang, Patrick Schäfer, John Paparrizos +1

Time-series segmentation, studied as either Change Point Detection (CPD) or State Detection (SD), underpins a broad range of monitoring and diagnostic applications. However, the field suffers from a persistent divide between the statistical community (focusing on CPD) and the machine learning and data mining communities (focusing on SD). Consequently, the literature remains fragmented: evaluations typically cover narrow methodological families, datasets are often small or homogeneous, and CPD and SD are rarely evaluated together. We address these gaps with TSB-SEG, a unified benchmark comprising 592 univariate and multivariate time series from 8 heterogeneous datasets and surveying 26 algorithms that span five decades of progress. To ensure a fair comparison, we introduce a two-pool design: a scalable main pool of tractable detectors used for the aggregate ranking, and an extended-scope pool for computationally intensive methods. All methods are unified under tsseg, an open-source library to ensure full reproducibility. Our systematic evaluation yields three insights: (a) no single detector dominates across domains, and per-dataset hyperparameter tuning remains decisive for…