Double Branch Model Based on Discrete Wavelet Transform for Spatiotemporal Prediction

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

In recent years, spatiotemporal sequence prediction has received increasing attention from researchers and has a wide range of promising applications in the fields of meteorology, traffic flow prediction, and autonomous driving. However, existing spatiotemporal sequence prediction models have some problems, such as slow convergence, training difficulties, and loss of image structural and detail information. We propose a novel end-To-end two-branch spatiotemporal sequence prediction model, which has been improved on these issues. We have compared our model with current advanced models using two datasets and found that our model reached or exceeded the level of the other advanced models in several metrics.

Original languageEnglish
Title of host publication2023 9th International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages105-110
Number of pages6
ISBN (Electronic)9798350342239
DOIs
StatePublished - 2023
Event9th International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2023 - Nanjing, China
Duration: 2 Jul 20234 Jul 2023

Publication series

Name2023 9th International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2023

Conference

Conference9th International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2023
Country/TerritoryChina
CityNanjing
Period2/07/234/07/23

Keywords

  • Attentional mechanisms
  • Discrete wavelet transform
  • Spatiotemporal sequence prediction

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