Modified Classification Method of Multivariate Time Series Based on Shapelets
МГТУ им. Н.Э. Баумана, Anatoly Karpenko, P. I. Sotnikov, МГТУ им. Н.Э. Баумана · Herald of the Bauman Moscow State Technical University Series Instrument Engineering · 2017
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 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.