TSB-SEG: A Systematic Time-Series Segmentation Benchmark

Time-series segmentation, studied as either \emph{Change Point Detection} (CPD) or \emph{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 \texttt{TSB-SEG}, a unified benchmark comprising $592$ univariate and multivariate time series across $8$ heterogeneous domains and surveying $27$ algorithms spanning five decades of progress. To ensure a fair comparison, we introduce a two-pool design: a scalable \emph{main pool} of sub-quadratic detectors used for the aggregate ranking, and an \emph{extended-scope pool} for computationally intensive specialized 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 performance, (b) CPD accuracy is a strong proxy for SD accuracy, and (c) data characteristics should guide method selection. We provide actionable guidance based on data properties, error tolerances, and tuning budgets, identify impactful hyperparameters, and characterize algorithm robustness to these choices.