深度学习在时空序列预测中的应用综述

Deep Learning for Spatio-Temporal Sequence Forecasting:A Survey

对深度学习模型应用于时空序列预测的最新进展进行总结.首先介绍时空序列数据的属性及类型,并进行相应的实例化与表示.接着针对时空序列数据存在的3个问题分别提出相应的数据预处理方法,对基于传统参数模型、传统机器学习模型以及深度学习模型的时空序列预测方法逐一阐述并对比分析,为研究者选择模型提供指导,之后总结深度学习模型在不同领域内对时空序列预测的应用.最后指出当前研究的不足以及时空序列预测进一步的研究方向.

In this paper,the latest progress of deep learning models in the application of spatio-temporal sequences prediction was summarized.First,the attributes and types of spatio-temporal sequence data,as well as the corresponding instantiation and representation,were introduced.Then,to deal with the three problems existed in spatio-temporal sequences,different data preprocessing methods were proposed,and the prediction methods based on traditional parameter models,traditional machine learning models and deep learning models were illustrated and compared,respectively,which provided a guidance for researchers to select proper models.Moreover,the application of deep learning models to spatio-temporal sequence prediction in different fields was depicted.Finally,the current research deficiencies and the future research directions for the spatio-temporal sequence prediction were suggested.