@Article{jbd.2021.016993,
AUTHOR = {Wei Fang, Yupeng Chen, Qiongying Xue},
TITLE = {Survey on Research of RNN-Based Spatio-Temporal Sequence Prediction  Algorithms},
JOURNAL = {Journal on Big Data},
VOLUME = {3},
YEAR = {2021},
NUMBER = {3},
PAGES = {97--110},
URL = {http://www.techscience.com/jbd/v3n3/45671},
ISSN = {2579-0056},
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.},
DOI = {10.32604/jbd.2021.016993}
}