多元时间序列分类是众多领域的关键问题，但是当前多元时序分类研究面临着原始数据高维、精度不足、可解释性缺乏等问题，这使得模型性能提升受限，准确率难以满足实际需求。针对上述问题，提出基于Shapelets的多元时间序列分类方法。首先，利用自适应邻居的无监督Shapelet学习将Shapelet变换与自适应权重结合，用于自动学习显著多元Shapelets；然后，将该方法与Shapelet相似性和类标约束项结合，增强模型可解释性和分类准确性；最后，提出模型的优化策略，用以获取最优的Shapelets，进一步提高模型的分类精度。与3种不同类型11个算法在11个公开数据集上进行比较，实验结果表明提出算法具有较高的分类精度。

Multivariate time series classification is a key problem in many fields, but the current research on multivariate time series classification is faced with some problems, such as high dimensionality of original data, low accuracy, and lack of interpretability, which limits the performance improvement of models and makes it difficult to meet the actual requirements. Aiming at above problem, a multivariate time series classification method based on Shapelets was proposed. Firstly, unsupervised Shapelet learning of adaptive neighbors was used to automatically learn significant multivariate Shapelets by combining Shapelets transform and adaptive weights. Then, the method was combined with Shapelet similarity and class label constraint to enhance the interpretability and classification accuracy of the model. Finally, the optimization strategy of the model was proposed to obtain the best Shapelets to further improve the classification accuracy of the model. Three different types of 11 algorithms were compared on 11 public data sets, and the experimental results show that the proposed algorithm has high classification accuracy.