硕士生导师
个人信息Personal Information
学历：博士研究生毕业
学位：工学博士学位
办公地点：犀浦3号教学楼31529
毕业院校：四川大学
学科：电子信息. 软件工程. 计算机应用技术
所在单位：计算机与人工智能学院
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Long sequence time-series forecasting with deep learning: A survey
影响因子：17.4
DOI码：10.1016/j.inffus.2023.101819
所属单位：西南交通大学
发表刊物：Information Fusion
刊物所在地：NETHERLANDS
关键字：Time series forecasting; Long time series forecasting; Transformer; Data mining; Deep learning
摘要：The development of deep learning technology has brought great improvements to the field of time series forecasting. Short sequence time-series forecasting no longer satisfies the current research community, and long-term future prediction is becoming the hotspot, which is noted as long sequence time-series forecasting (LSTF). The LSTF has been widely studied in the extant literature, but few reviews of its research development are reported. In this article, we provide a comprehensive survey of LSTF studies with deep learning technology. We propose rigorous definitions of LSTF and summarize the evolution in terms of a proposed taxonomy based on network structure. Next, we discuss three key problems and corresponding solutions from long dependency modeling, computation cost, and evaluation metrics. In particular, we propose a Kruskal–Wallis test based evaluation method for evaluation metrics problems. We further synthesize the applications, datasets, and open-source codes of LSTF. Moreover, we conduct extensive case studies comparing the proposed Kruskal–Wallis test based evaluation method with existing metrics and the results demonstrate the effectiveness. Finally, we propose potential research directions in this rapidly growing field. All resources and codes are assembled and organized under a unified framework that is available online at https://github.com/Masterleia/TSF_LSTF_Compare.
合写作者：Minbo Ma,Tianrui Li,Hongjun Wang
第一作者：Zonglei Chen
论文类型：SCI
通讯作者：Chongshou Li
学科门类：工学
文献类型：J
卷号：97
页面范围：101819
ISSN号：1566-2535
是否译文：否
发表时间：2023-04-21
收录刊物：SCI