The classification of multidimensional time series holds significant importance across various domains, including action classification, medical diagnosis, and credit assessment. Within multidimensional time series data, features pertinent to classification exhibit variance in their positional distribution along the entirety of the sequence. Moreover, the relative significance of features across distinct dimensions also fluctuates, contributing to suboptimal performance in multidimensional time series classification. Consequently, the proposition of tailored deep learning models for feature extraction specific to multidimensional time series data becomes imperative. This paper introduces attention mechanisms applied to the temporal dimension, graph attention mechanisms for inter-dimensional relationships within multidimensional data, and attention mechanisms applied between channels post-convolutional calculations. These mechanisms are deployed for feature extraction across temporal, variational, and channel dimensions of multidimensional time series data, respectively. Furthermore, attention is directed towards inter-channel interactions within the squeeze-and-excitation network to enhance the model’s representational capacity. Experimental findings substantiate the viability of integrating attention mechanisms into multidimensional time series classification endeavors.

多维时间序列的分类在各个领域都具有重要意义，包括行动分类、医学诊断和信用评估。在多维时间序列数据中，与分类相关的特征在整个序列中的位置分布表现出差异。此外，跨不同维度的特征的相对重要性也会波动，从而导致多维时间序列分类的性能欠佳。因此，为特定于多维时间序列数据的特征提取量身定制的深度学习模型的提议变得势在必行。本文介绍了应用于时间维度的注意力机制、多维数据中维度间关系的图注意力机制，以及卷积后计算后通道之间应用的注意力机制。这些机制分别用于跨多维时间序列数据的时间、变分和通道维度进行特征提取。此外，注意力被引导到挤压和激励网络内的通道间相互作用上，以增强模型的表征能力。实验结果证实了将注意力机制整合到多维时间序列分类工作的可行性。