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Survey on Research of RNN-Based Spatio-Temporal Sequence Prediction Algorithms

Wei Fang1,2,*, Yupeng Chen1, Qiongying Xue1

1 School of Computer & Software, Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science & Technology, Nanjing, 210044, China
2 Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou, 215006, China

* Corresponding Author:Wei Fang. Email: email

Journal on Big Data 2021, 3(3), 97-110.


In the past few years, deep learning has developed rapidly, and many researchers try to combine their subjects with deep learning. The algorithm based on Recurrent Neural Network (RNN) has been successfully applied in the fields of weather forecasting, stock forecasting, action recognition, etc. because of its excellent performance in processing Spatio-temporal sequence data. Among them, algorithms based on LSTM and GRU have developed most rapidly because of their good design. This paper reviews the RNN-based Spatiotemporal sequence prediction algorithm, introduces the development history of RNN and the common application directions of the Spatio-temporal sequence prediction, and includes precipitation nowcasting algorithms and traffic flow forecasting algorithms. At the same time, it also compares the advantages and disadvantages, and innovations of each algorithm. The purpose of this article is to give readers a clear understanding of solutions to such problems. Finally, it prospects the future development of RNN in the Spatio-temporal sequence prediction algorithm.


Cite This Article

APA Style
Fang, W., Chen, Y., Xue, Q. (2021). Survey on research of rnn-based spatio-temporal sequence prediction algorithms. Journal on Big Data, 3(3), 97-110.
Vancouver Style
Fang W, Chen Y, Xue Q. Survey on research of rnn-based spatio-temporal sequence prediction algorithms. J Big Data . 2021;3(3):97-110
IEEE Style
W. Fang, Y. Chen, and Q. Xue "Survey on Research of RNN-Based Spatio-Temporal Sequence Prediction Algorithms," J. Big Data , vol. 3, no. 3, pp. 97-110. 2021.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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