C. Coelho

Unsupervised state-learning for multivariate time series data: a survey and practical guidelines

An evaluation and reporting protocol tailored to state-learning is proposed, emphasising leakage-free temporal splits, temporal coherence, interpretability, complexity control and uncertainty calibration, and widely used benchmark testbeds that provide evaluation-only labels are summarized.