Time Series Classification (TSC) is an important and challenging task for many visual computing applications. Despite the extensive range of methods developed for TSC, relatively few utilized Deep Neural Networks (DNNs). In this paper, we propose two novel attention blocks (Global Temporal Attention and Temporal Pseudo-Gaussian augmented Self-Attention) that can enhance deep learning-based TSC approaches, even when such approaches are designed and optimized for a specific dataset or task. We validate this claim by evaluating multiple state-of-the-art deep learning-based TSC models on the University of East Anglia (UEA) benchmark, a standardized collection of 30 Multivariate Time Series Classification (MTSC) datasets. We show that adding the proposed attention blocks improves base models' average accuracy by up to 3.6%. Additionally, the proposed TPS block uses a new injection module to include the relative positional information in transformers. As a standalone unit with less computational complexity, it enables TPS to perform better than most of the state-of-the-art DNN-based TSC methods. The source codes for our experimental setups and proposed attention blocks are made publicly available.

翻译：时间序列分类（TSC）是许多视觉计算应用中的重要且具有挑战性的任务。尽管已开发出大量用于TSC的方法，但利用深度神经网络（DNN）的方法相对较少。本文提出了两种新颖的注意力模块（全局时间注意力与时间伪高斯增强自注意力），它们能够增强基于深度学习的TSC方法，即使这些方法针对特定数据集或任务进行设计和优化。我们通过在东英吉利大学（UEA）基准（由30个多元时间序列分类（MTSC）数据集组成的标准化集合）上评估多个最先进的基于深度学习的TSC模型来验证这一主张。结果表明，添加所提出的注意力模块可使基础模型的平均准确率提升高达3.6%。此外，所提出的TPS模块使用新的注入机制将相对位置信息纳入Transformer中。作为一个计算复杂度较低的独立单元，它使TPS的性能优于大多数最先进的基于DNN的TSC方法。我们公开提供了实验设置和所提出注意力模块的源代码。