ELIS++: a shapelet learning approach for accurate and efficient time series classification

Abstract

In recent years, time series classification with shapelets, due to the high accuracy and good interpretability, has attracted considerable interests. These approaches extract or learn shapelets from the training time series. Although they can achieve higher accuracy than other approaches, there still confront some challenges. First, they may suffer from low accuracy in the case of small training dataset. Second, they must manually set some parameters, like the number of shapelets and the length of each shapelet beforehand, and some hyper-parameters, like learning rate and regulation weight, which are difficult to set without prior knowledge. Third, extracting or learning shapelets incurs a huge computation cost, due to the huge search space. In this paper, we extend our previous shapelet learning approach ELIS to ELIS++. To improve the accuracy on the small training dataset, we propose a data augmentation approach. To learn the higher quality shapelets, based on the PAA shapelet candidates search technique proposed in ELIS, ELIS++ first propose a novel entropy-based approach shapelet candidate selection mechanism to discover shapelet candidates, and then applies the logistic regression model to adjust shapelets.To avoid setting other parameters manually, we propose a Bayesian Optimization based approach. Moreover, two techniques are proposed to improve the efficiency, coarse-grained shapelet adjustment and SIMD-based parallel computation. We conduct extensive experiments on 35 UCR datasets, and results verify the effectiveness and efficiency of ELIS++.