Transformer architecture has widespread applications, particularly in Natural Language Processing and computer vision. Recently Transformers have been employed in various aspects of time-series analysis. This tutorial provides an overview of the Transformer architecture, its applications, and a collection of examples from recent research papers in time-series analysis. We delve into an explanation of the core components of the Transformer, including the self-attention mechanism, positional encoding, multi-head, and encoder/decoder. Several enhancements to the initial, Transformer architecture are highlighted to tackle time-series tasks. The tutorial also provides best practices and techniques to overcome the challenge of effectively training Transformers for time-series analysis.

翻译：Transformer架构在自然语言处理和计算机视觉等领域具有广泛应用。近年来，Transformer已被应用于时间序列分析的多个方面。本教程概述了Transformer架构及其应用，并汇集了近期时间序列分析研究论文中的典型案例。我们深入解析了Transformer的核心组件，包括自注意力机制、位置编码、多头机制以及编码器/解码器结构。针对时间序列任务，本文重点介绍了对原始Transformer架构的若干改进方案。此外，本教程还提供了有效训练时间序列分析中Transformer模型的最佳实践与关键技术。