[1]FAN Hangzhou,MEI Hongyan,ZHAO Qin,et al.Multivariate time series forecasting with a graph neural network and dual attention mechanism[J].CAAI Transactions on Intelligent Systems,2024,19(5):1277-1286.[doi:10.11992/tis.202305020]
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Multivariate time series forecasting with a graph neural network and dual attention mechanism
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
19
Number of periods:
2024 5
Page number:
1277-1286
Column:
学术论文—人工智能基础
Public date:
2024-09-05
- Title:
- Multivariate time series forecasting with a graph neural network and dual attention mechanism
- Keywords:
- multivariate time series forecasting; graph neural network; attention mechanism; feature fusion; temporal convolutional network; deep learning; convolutional neural network; spatiotemporal features
- CLC:
- TP391
- DOI:
- 10.11992/tis.202305020
- Abstract:
- To address the issues of insufficient capture of dependency relationships between variables and inadequate utilization of multichannel temporal information in multivariate time series, a forecasting model fused with a dual attention mechanism (FFDA-GNN) is proposed. This model integrates a graph neural network with a spatial attention mechanism to enhance the capture of dependencies among multiple variables. Additionally, parallel multilayer inflation convolution and channel attention mechanisms are used for multi-channel feature extraction from temporal data. This approach fully utilizes multi-channel information and enhances forecasting performance. Comparative experiments conducted on datasets from the fields of economics, electric power, and traffic show that the proposed model achieves better prediction accuracy than other benchmark methods and demonstrates superior feasibility.
- References:
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Last Update:
2024-09-05