Shapelets are discriminative subsequences extracted from time-series data. Classifiers using shapelets have proven to achieve performances competitive to state-of-the-art methods, while enhancing the model’s interpretability. While a lot of research has been done for univariate time-series shapelets, extensions for the multivariate setting have not yet received much attention. To extend shapelets-based classification to a multidimensional setting, we developed a novel architecture for shapelets learning, by embedding them as trainable weights in a multi-layer Neural Network. We also investigated the introduction of a novel learning strategy for the shapelets, comprising of two additional terms in the optimization goal, to retrieve a reduced set of uncorrelated shapelets. This paper describes the proposed architecture and presents results on ten publicly available benchmark datasets, as well as a comparison with existing state-of-the-art methods. Moreover, the proposed optimization objective leads the model to automatically select smaller sets of uncorrelated shapelets, thus requiring no additional manual optimization on typically important hyper-parameters such as number and length of shapelets. The results show how the proposed approach achieves competitive performance across the datasets, and always leads to a significant reduction in the number of shapelets used. This can make it faster for a domain expert to match shapelets to real patterns, thus enhancing the interpretability of the model. Finally, since the shapelets learnt during training can be extracted from the model they can serve as meaningful insights on the classifier’s decisions and the interactions between different dimensions.

小波是从时间序列数据中提取的区分性子序列。经证明，使用shapelet的分类器可实现与最新方法相抗衡的性能，同时增强了模型的可解释性。尽管已经对单变量时间序列小形进行了大量研究，但对多元设置的扩展还没有引起足够的重视。为了将基于形状的分类扩展到多维环境，我们开发了一种用于形状学习的新颖架构，将它们作为可训练的权重嵌入到多层神经网络中。我们还研究了针对小形状的新颖学习策略的引入，该策略在优化目标中包含两个附加术语，以检索减少的一组不相关的小形状。本文介绍了拟议的体系结构，并在十个可公开获得的基准数据集上给出了结果，并与现有的最新方法进行了比较。此外，提出的优化目标使模型自动选择较小的一组不相关的形状，因此不需要对通常重要的超参数（例如形状的数量和长度）进行额外的手动优化。结果表明，所提出的方法如何在整个数据集上实现竞争优势，并始终大大减少所使用的形状的数量。这可以使领域专家更快地将shapelet与实际模式匹配，从而增强模型的可解释性。最后，由于可以从模型中提取在训练过程中学习到的形状，因此它们可以用作分类器决策和不同维度之间相互作用的有意义的见解。