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Parallel multi-scale dynamic graph neural network for multivariate time series forecasting
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AI
DOI：10.1016/j.patcog.2024.111037.png)
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Abstract
En 中文
Accurately modeling and predicting multivariate time series (MTS) are crucial in real-world scenarios, where precise predictions aid decision-making. MTS exhibit complex variable relationships and dynamic evolution patterns across various time scales, posing significant modeling challenges. To address these, a parallel multiscale dynamic graph neural network (PMEDGN) is proposed for parallel modeling of dynamic information across multiple time scales. Spatial-temporal embedding module based on spatial-temporal attention mechanisms and multi-scale dynamic graph generation module are designed. These modules capture implicit spatialtemporal dependencies and self-learn to generate multi-scale dynamic graph structures from MTS without predefined graphs, automatically obtaining the importance of different time scale patterns. Additionally, a global graph convolution module is developed to integrate parallel multi-scale information, enhancing collaboration across time scales for final predictions. Comprehensive experiments on real-world datasets demonstrate the effectiveness and superiority of PMEDGN over state-of-the-art methods, underscoring its potential for practical applications.
Keywords:
Multivariate time series forecasting
Parallel multi-scale dynamic modeling
Parallel multi-scale information fusion
Graph neural network
Graph structure learning