Times series classification can be successfully tackled by jointly learning a shapelet-based representation of the series in the dataset and classifying the series according to this representation. However, although the learned shapelets are discriminative, they are not always similar to pieces of a real series in the dataset. This makes it difficult to interpret the decision, i.e. difficult to analyze if there are particular behaviors in a series that triggered the decision. In this paper, we make use of a simple convolutional network to tackle the time series classification task and we introduce an adversarial regularization to constrain the model to learn more interpretable shapelets. Our classification results on all the usual time series benchmarks are comparable with the results obtained by similar state-of-the-art algorithms but our adversarially regularized method learns shapelets that are, by design, interpretable.

K and are Gaussian distributed. Besides, we assume that m 1 K and are similarly distributed for each AP m [2] . With equation (3), we estimate the coordinates of different users according to the received signal strength of the different users at the APs.

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++.

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