《计算机应用研究》|Application Research of Computers

时空序列预测方法综述

Survey of spatio-temporal sequence prediction methods

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作者 黎维,陶蔚,周星宇,潘志松
机构 陆军工程大学 a.指挥控制工程学院;b.通信工程学院,南京 210007
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文章编号 1001-3695(2020)10-001-2881-08
DOI 10.19734/j.issn.1001-3695.2019.05.0184
摘要 随着数据采集技术的进步,带有地理位置信息的时空数据迅速增长,迫切需要探索有效的时空数据建模方法。时空序列预测是时空数据建模的基础方法之一,它广泛应用于很多领域。目前缺乏对它进行综述的中文文献,因而对这些方法进行归纳和总结具有重要的研究意义。针对时空序列预测问题进行了研究,首先回顾了其应用背景和发展历程,介绍了它的相关定义及特点。然后按其类别介绍了传统的时空序列预测方法、基于传统机器学习的时空序列预测方法和基于深度学习的时空序列预测方法,并分析了这些方法的应用范围和优缺点。最后对时空序列预测未来的研究方向进行了梳理和展望,为研究者们进一步深入研究时空序列预测问题奠定了理论基础。
关键词 机器学习; 深度学习; 时空数据; 时空序列预测
基金项目 国家自然科学基金资助项目
本文URL http://www.arocmag.com/article/01-2020-10-001.html
英文标题 Survey of spatio-temporal sequence prediction methods
作者英文名 Li Wei, Tao Wei, Zhou Xingyu, Pan Zhisong
机构英文名 a.College of Command & Control Engineering,b.College of Communication Engineering,Army Engineering University of PLA,Nanjing 210007,China
英文摘要 With the advancement of data acquisition technology, spatiotemporal data with geographic location information are growing rapidly, so it is urgent to explore effective spatiotemporal data modeling methods. Spatiotemporal sequence prediction is one of the basic methods in spatiotemporal data modeling and widely used in many fields. At present, there is no Chinese literature on its review, so it is of great significance to summarize these methods. For the spatiotemporal sequence prediction problem, this paper reviewed its application background and development history. Then it introduced some related definitions and characteristics. According to its category, the spatiotemporal sequence prediction methods, based on traditional methods, classical machine learning methods and deep learning methods are analyzed respectively as well as their application scope, advantages and disadvantages. Finally, this paper made a review and prospect over the research directions of spatiotemporal sequence prediction, laying a theoretical foundation for researchers to further study this problem.
英文关键词 machine learning; deep learning; spatiotemporal data; spatiotemporal sequence prediction
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收稿日期 2019/5/28
修回日期 2019/7/17
页码 2881-2888
中图分类号 TP181
文献标志码 A