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Time series data remains a perennially important datatype considered in data mining. In the last decade there has been an increasing realization that time series data can be best understood by reasoning about time series subsequences on the basis of their similarity to other subsequences: the two most familiar such time series concepts being motifs and discords. Time series motifs refer to two particularly close subsequences, whereas time series discords indicate subsequences that are far from their nearest neighbors. However, we argue that it can sometimes be useful to simultaneously reason about a subsequence’s closeness to certain data and its distance to other data. In this work we introduce a novel primitive called the Contrast Profile that allows us to efficiently compute such a definition in a principled way. As we will show, the Contrast Profile has many downstream uses, including anomaly detection, data exploration, and preprocessing unstructured data for classification. We demonstrate the utility of the Contrast Profile by showing how it allows end-to-end classification in datasets with tens of billions of datapoints, and how it can be used to explore datasets and reveal subtle patterns that might otherwise escape our attention. Moreover, we demonstrate the generality of the Contrast Profile by presenting detailed case studies in domains as diverse as seismology, animal behavior, and cardiology.