The emergence of deep learning has yielded noteworthy advancements in time
series forecasting (TSF). Transformer architectures, in particular, have
witnessed broad utilization and adoption in TSF tasks. Transformers have proven
to be the most successful solution to extract the semantic correlations among
the elements within a long sequence. Various variants have enabled transformer
architecture to effectively handle long-term time series forecasting (LTSF)
tasks. In this article, we first present a comprehensive overview of
transformer architectures and their subsequent enhancements developed to
address various LTSF tasks. Then, we summarize the publicly available LTSF
datasets and relevant evaluation metrics. Furthermore, we provide valuable
insights into the best practices and techniques for effectively training
transformers in the context of time-series analysis. Lastly, we propose
potential research directions in this rapidly evolving field.

深度学习在时间序列预测方面取得了显著进展，其中 Transformer 架构在处理长序列的语义相关性方面表现出色。研究综述了 Transformer 架构及其改进方法在长期时间序列预测任务中的应用，总结了公开的长期时间序列预测数据集和评估指标，并提供了关于在时间序列分析中有效训练 transformers 的最佳实践和技术，最后提出了该领域的潜在研究方向。