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Article

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

1
School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, China
2
College of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China
3
Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Kyandoghere Kyamakya
Sensors 2022, 22(15), 5877; https://doi.org/10.3390/s22155877
Received: 30 June 2022 / Revised: 28 July 2022 / Accepted: 3 August 2022 / Published: 5 August 2022
(This article belongs to the Section Intelligent Sensors)
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-based traffic time series forecasting. Our model inherits the benefits of MF and regularizer optimization and further carries out the compensatory modeling of the spatial-temporal correlations through a well-designed bi-directional residual structure. Of particular concern is that MF modeling and later residual learning share and synchronize iterative updates as equal training parameters, which considerably alleviates the error propagation problem that associates with rolling forecasting. Besides, most of the existing prediction models have neglected the difficult-to-avoid issue of missing traffic data; the ST-CRMF model can repair the possible missing value while fulfilling the forecasting tasks. After testing the effects of key parameters on model performance, the numerous experimental results confirm that our ST-CRMF model can efficiently capture the comprehensive spatial-temporal dependencies and significantly outperform those state-of-the-art models in the short-to-long terms (5-/15-/30-/60-min) traffic forecasting tasks on the open Seattle-Loop and METR-LA traffic datasets. View Full-Text
Keywords: traffic time series forecasting; matrix factorization; residual learning; missing value traffic time series forecasting; matrix factorization; residual learning; missing value
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MDPI and ACS Style

Li, J.; Wu, P.; Li, R.; Pian, Y.; Huang, Z.; Xu, L.; Li, X. ST-CRMF: Compensated Residual Matrix Factorization with Spatial-Temporal Regularization for Graph-Based Time Series Forecasting. Sensors 2022, 22, 5877. https://doi.org/10.3390/s22155877

AMA Style

Li J, Wu P, Li R, Pian Y, Huang Z, Xu L, Li X. ST-CRMF: Compensated Residual Matrix Factorization with Spatial-Temporal Regularization for Graph-Based Time Series Forecasting. Sensors. 2022; 22(15):5877. https://doi.org/10.3390/s22155877

Chicago/Turabian Style

Li, Jinlong, Pan Wu, Ruonan Li, Yuzhuang Pian, Zilin Huang, Lunhui Xu, and Xiaochen Li. 2022. "ST-CRMF: Compensated Residual Matrix Factorization with Spatial-Temporal Regularization for Graph-Based Time Series Forecasting" Sensors 22, no. 15: 5877. https://doi.org/10.3390/s22155877

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