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Time series classification (TSC) aims to predict the class label of a giventime series, which is critical to a rich set of application areas such aseconomics and medicine. State-of-the-art TSC methods have mostly focused onclassification accuracy and efficiency, without considering theinterpretability of their classifications, which is an important propertyrequired by modern applications such as appliance modeling and legislation suchas the European General Data Protection Regulation. To address this gap, wepropose a novel TSC method - the Randomized-Supervised Time Series Forest(r-STSF). r-STSF is highly efficient, achieves state-of-the-art classificationaccuracy and enables interpretability. r-STSF takes an efficient interval-basedapproach to classify time series according to aggregate values ofdiscriminatory sub-series (intervals). To achieve state-of-the-art accuracy,r-STSF builds an ensemble of randomized trees using the discriminatorysub-series. It uses four time series representations, nine aggregationfunctions and a supervised binary-inspired search combined with a featureranking metric to identify highly discriminatory sub-series. The discriminatorysub-series enable interpretable classifications. Experiments on extensivedatasets show that r-STSF achieves state-of-the-art accuracy while being ordersof magnitude faster than most existing TSC methods. It is the only classifierfrom the state-of-the-art group that enables interpretability. Our findingsalso highlight that r-STSF is the best TSC method when classifying complex timeseries datasets.

Fast, Accurate and Interpretable Time Series Classification Through Randomization