Noname manuscript No. (will be inserted by the editor) Fast Classification of Univariate and Multivariate Time series Through Shapelets Discovery

Abstract Time-series classification is an important problem for the data min-ing community due to the wide range of application domains involving time-series data. A recent paradigm, called shapelets, represents patterns that are highly predictive for the target variable. Shapelets are discovered by measur-ing the prediction accuracy of a set of potential (shapelet) candidates. The candidates typically consist of all the segments of a dataset, therefore, the dis-covery of shapelets is computationally expensive. This paper proposes a novel method that avoids measuring the prediction accuracy of similar candidates in Euclidean distance space, through an online clustering/pruning technique. In addition, our algorithm incorporates a supervised shapelet selection that filters out only those candidates that improve classification accuracy. Empirical evi-dence on 45 univariate datasets from the UCR collection demonstrate that our method is 3-4 orders of magnitudes faster than the fastest existing shapelet-discovery method, while providing better prediction accuracy. In addition, we extended our method to multivariate time-series data. Runtime results over 4 real-life multivariate datasets indicate that our method can classify MB-scale data in a matter of seconds and GB-scale data in a matter of minutes. The achievements do not compromise quality, on the contrary, our method is even superior to the multivariate baseline in terms of classification accuracy