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

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. bec...

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Bibliographic Details
Published in:Journal on big data (Henderson, Nev.) Vol. 3; no. 3; pp. 97 - 110
Main Authors: Fang, Wei, Chen, Yupeng, Xue, Qiongying
Format: Journal Article
Language:English
Published: Henderson Tech Science Press 2021
Subjects:
Algorithms
Deep learning
Machine learning
Nowcasting
Recurrent neural networks
Traffic flow
Weather forecasting
ISSN:2579-0056, 2579-0048, 2579-0056
Online Access:Get full text
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Summary: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.
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ISSN:2579-0056
2579-0048
2579-0056
DOI:10.32604/jbd.2021.016993