CODiT: Conformal Out-of-Distribution Detection in Time-Series Data for Cyber-Physical Systems

Uncertainty in the predictions of learning enabled components hinders their deployment in safety-critical cyber-physical systems (CPS). A shift from the training distribution of a learning enabled component (LEC) is one source of uncertainty in the LEC's predictions. Detection of this shift or out-of-distribution (OOD) detection on individual datapoints has therefore gained attention recently. But in many applications, inputs to CPS form a temporal sequence. Existing techniques for OOD detection in time-series data for CPS either do not exploit temporal relationships in the sequence or do not provide any guarantees on detection. We propose using deviation from the in-distribution temporal equivariance as the non-conformity measure in conformal anomaly detection framework for OOD detection in time-series data for CPS. Computing independent predictions from multiple conformal detectors based on the proposed measure and combining these predictions by Fisher's method leads to the proposed detector CODiT with bounded false alarms. We illustrate the efficacy of CODiT by achieving state-of-the-art results in autonomous driving systems with perception (or vision) LEC. We also perform experiments on medical CPS for GAIT analysis where physiological (non-vision) data is collected with force-sensitive resistors attached to the subject's body. Code, data, and trained models are available at https://github.com/kaustubhsridhar/time-series-OOD