MGCP: A Multi-Grained Correlation based Prediction Network for multivariate time series
Chen, Zhicheng Xiao, Xi Xu, Ke Zhang, Zhong Rong, Yu Li, Qing Gan, Guojun Xu, Zhiqiang Zhao, Peilin
Chen, Zhicheng
Xiao, Xi
Xu, Ke
Zhang, Zhong
Rong, Yu
Li, Qing
Gan, Guojun
Xu, Zhiqiang
Zhao, Peilin
Supervisor
Department
Machine Learning
Embargo End Date
Type
Journal article
Date
2025
License
Language
English
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Research Projects
Organizational Units
Journal Issue
Abstract
Multivariate time series prediction is widely used in daily life, which poses significant challenges due to the complex correlations that exist at multi-grained levels. Unfortunately, the majority of current time series prediction models fail to simultaneously learn the correlations of multivariate time series at multi-grained levels, resulting in suboptimal performance. To address this, we propose a Multi-Grained Correlations-based Prediction (MGCP) Network, which simultaneously considers the correlations at three granularity levels to enhance prediction performance. Specifically, MGCP utilizes Adaptive Fourier Neural Operators and Graph Convolutional Networks to learn the global spatiotemporal correlations and inter-series correlations, enabling the extraction of potential features from multivariate time series at fine-grained and medium-grained levels. Additionally, MGCP employs adversarial training with an attention mechanism-based predictor and conditional discriminator to optimize prediction results at coarse-grained level, ensuring high fidelity between the generated forecast results and the actual data distribution. Finally, we compare MGCP with several state-of-the-art time series prediction algorithms on real-world benchmark datasets, and our results demonstrate the generality and effectiveness of the proposed model. © 2025 Elsevier B.V.
Citation
Z. Chen et al., “MGCP: A Multi-Grained Correlation based Prediction Network for multivariate time series,” Neurocomputing, vol. 649, p. 130751, Oct. 2025, doi: 10.1016/J.NEUCOM.2025.130751
Source
Neurocomputing
Conference
Keywords
Adaptive Fourier neural operators, Attention mechanism, Generative adversarial networks, Graph convolutional networks, Multi-grained correlations, Time series prediction
Subjects
Source
Publisher
Elsevier