Predictive Surprise as Self-Grounding Concept Bottleneck for Interpretable Time Series

Time-series classifiers deployed in risk-sensitive domains such as healthcare, wearable sensing, and industrial monitoring must be accurate, interpretable, and capable of abstaining when evidence is weak. No existing method delivers all three, and the obstacle is structural, rather than logistical. Meaningful temporal patterns cannot be defined without knowing segment boundaries, yet meaningful boundaries cannot be placed without knowing which patterns to expect. Existing approaches break this circularity through fixed windows or expert-annotated vocabularies, sacrificing at least one of the three properties. We introduce ConceptTime, which closes the loop through a single self-supervised signal. A frozen probabilistic forecaster predicts a distribution over the next observation at every timestep of the sequence. We call the mismatch between its prediction and the realized signal, the predictive surprise. This signal both locates segment boundaries and characterizes each resulting segment through the forecaster's own predictive statistics. Segments are thus grounded by \emph{how} they behaved relative to expectation, not by what they look like. These summaries cluster into a vocabulary of dynamical regimes that we call concepts. The vocabulary distinguishes calm-after-spike from calm-after-calm, and unifies visually distinct segments that violate expectation in the same way. A lightweight head classifies from the concept sequence, and the distance to the nearest concept yields per-input reliability for free. Across seventeen UEA, human-activity, biomedical, and fault-diagnosis benchmarks, ConceptTime matches state-of-the-art black-box accuracy, beats every interpretable baseline, and retains near 98% accuracy on Epilepsy with 1% of labels. It equals or exceeds softmax, prototype-distance, and SHAP/TimeX/TimeX++ on OOD detection and deletion-faithfulness, without a single annotation, language-model call, or domain expert.