ST-CRMF: Compensated Residual Matrix Factorization with Spatial-Temporal Regularization for Graph-Based Time Series Forecasting

Despite the extensive efforts, accurate traffic time series forecasting remains challenging. By taking into account the non-linear nature of traffic in-depth, we propose a novel ST-CRMF model consisting of the Compensated Residual Matrix Factorization with Spatial-Temporal regularization for graph-b...

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Detalles Bibliográficos
Autores principales: Li, Jinlong, Wu, Pan, Li, Ruonan, Pian, Yuzhuang, Huang, Zilin, Xu, Lunhui, Li, Xiaochen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9371056/
https://www.ncbi.nlm.nih.gov/pubmed/35957433
http://dx.doi.org/10.3390/s22155877

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9371056/
https://www.ncbi.nlm.nih.gov/pubmed/35957433
http://dx.doi.org/10.3390/s22155877

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