Open Access
ARTICLE
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:
Journal on Big Data 2021, 3(3), 97-110. https://doi.org/10.32604/jbd.2021.016993
Received 17 January 2021; Accepted 08 April 2021; Issue published 22 November 2021
Abstract
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.
Keywords
Cite This Article
W. Fang, Y. Chen and Q. Xue, "Survey on research of rnn-based spatio-temporal sequence prediction algorithms,"
Journal on Big Data, vol. 3, no.3, pp. 97–110, 2021. https://doi.org/10.32604/jbd.2021.016993