Fast classification of univariate and multivariate time series through shapelet discovery

Abstract

Time-series classification is an important problem for the data mining 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 measuring the prediction accuracy of a set of potential (shapelet) candidates. The candidates typically consist of all the segments of a dataset; therefore, the discovery 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 evidence on 45 univariate datasets from the UCR collection demonstrates 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 four 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.