A Systematic Review for Transformer-based Long-term Series Forecasting
- URL: http://arxiv.org/abs/2310.20218v1
- Date: Tue, 31 Oct 2023 06:37:51 GMT
- Title: A Systematic Review for Transformer-based Long-term Series Forecasting
- Authors: Liyilei Su, Xumin Zuo, Rui Li, Xin Wang, Heng Zhao and Bingding Huang
- Abstract summary: Transformer architecture has proven to be the most successful solution to extract semantic correlations.
Various variants have enabled transformer architecture to handle long-term time series forecasting tasks.
- Score: 7.414422194379818
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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