Recently, deep neural networks (DNNs) have achieved excellent performance on time series classification. However, DNNs require large amounts of labeled data for supervised training. Although data augmentation can alleviate this problem, the standard approach assigns the same label to all augmented samples from the same source. This leads to the expansion of the data distribution such that the classification boundaries may be even harder to determine. In this paper, we propose Joint-label learning by Dual Augmentation (JobDA), which can enrich the training samples without expanding the distribution of the original data. Instead, we apply simple transformations to the time series and give these modified time series new labels, so that the model has to distinguish between these and the original data, as well as separating the original classes. This approach sharpens the boundaries around the original time series, and results in superior classification performance. We use Time Series Warping for our transformations: We shrink and stretch different regions of the original time series, like a fun-house mirror. Experiments conducted on extensive time-series datasets show that JobDA can improve the model performance on small datasets. Moreover, we verify that JobDA has better generalization ability compared with conventional data augmentation, and the visualization analysis further demonstrates that JobDA can learn more compact clusters.

Time series clustering is usually an essential unsupervised task in cases when category information is not available and has a wide range of applications. However, existing time series clustering methods usually either ignore temporal dynamics of time series or isolate the feature extraction from clustering tasks without considering the interaction between them. In this article, a time series clustering framework named self-supervised time series clustering network (STCN) is proposed to optimize the feature extraction and clustering simultaneously. In the feature extraction module, a recurrent neural network (RNN) conducts a one-step time series prediction that acts as the reconstruction of the input data, capturing the temporal dynamics and maintaining the local structures of the time series. The parameters of the output layer of the RNN are regarded as model-based dynamic features and then fed into a self-supervised clustering module to obtain the predicted labels. To bridge the gap between these two modules, we employ spectral analysis to constrain the similar features to have the same pseudoclass labels and align the predicted labels with pseudolabels as well. STCN is trained by iteratively updating the model parameters and the pseudoclass labels. Experiments conducted on extensive time series data sets show that STCN has state-of-the-art performance, and the visualization analysis also demonstrates the effectiveness of the proposed model.

Shapelets are discriminative subsequences for time series classification. Recently, learning time-series shapelets (LTS) was proposed to learn shapelets by gradient descent directly. Although learning-based shapelet methods achieve better results than previous methods, they still have two shortcomings. First, the learned shapelets are fixed after training and cannot adapt to time series with deformations at the testing phase. Second, the shapelets learned by back-propagation may not be similar to any real subsequences, which is contrary to the original intention of shapelets and reduces model interpretability. In this paper, we propose a novel shapelet learning model called Adversarial Dynamic Shapelet Networks (ADSNs). An adversarial training strategy is employed to prevent the generated shapelets from diverging from the actual subsequences of a time series. During inference, a shapelet generator produces sample-specific shapelets, and a dynamic shapelet transformation uses the generated shapelets to extract discriminative features. Thus, ADSN can dynamically generate shapelets that are similar to the real subsequences rather than having arbitrary shapes. The proposed model has high modeling flexibility while retaining the interpretability of shapelet-based methods. Experiments conducted on extensive time series data sets show that ADSN is state-of-the-art compared to existing shapelet-based methods. The visualization analysis also shows the effectiveness of dynamic shapelet generation and adversarial training.

Shapelets are discriminative subsequences for time series classification (TSC). Although shapelet-based methods have achieved good performance and interpretability, they still have two issues that can be improved. First, previous methods only assess a shapelet by how accurately it can classify all the samples. However, for multi-class imbalanced classification tasks, these methods will ignore the shapelets that can distinguish minority class from other classes and will tend to use the shapelets that are useful for discriminating the majority classes. Second, the shapelets are fixed after the training phase and cannot adapt to time series with deformations, which will lead to poor matches to the shapelets. In this paper, we propose a novel end-to-end shapelet learning model called Triple Shapelet Networks (TSNs) to extract multi-level feature representations. Specifically, TSN learns the most discriminative shapelets by gradient descent similar to previous methods. In addition, it learns category-specific shapelets for each class by using auxiliary binary classifiers. Finally, it uses a shapelet generator to produce sample-specific shapelets conditioned on subsequences of the input time series. The addition of category-level and sample-level shapelets to the standard model improves the performance. Experiments conducted on extensive time series data sets show that TSN is state-of-the-art compared to existing shapelet-based methods, and the visualization analysis also shows its effectiveness.