Machine learning models are prone to make incorrect predictions on inputs that are far from the training distribution. This hinders their deployment in safety-critical domains such as autonomous vehicles and healthcare. A number of techniques have been proposed for out-of-distribution (OOD) detection on individual datapoints. But in many applications, the inputs to these models form a temporal sequence. Existing techniques for OOD detection in time-series either do not exploit temporal relationships in the sequence or do not provide any guarantees on detection. We develop a self-supervised learning approach, CODiT for OOD detection in time-series data with guarantees on detection. We illustrate CODiT's efficacy on autonomous driving vision datasets and physiological GAIT data. Our code is available at shorturl.at/fzR02.