TY - GEN
T1 - Graph Neural Network-Enhanced Multivariate Time Series Forecasting with Series-Core Fusion
AU - Hou, Yuntian
AU - Zhang, Di
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - This study introduces Starformer, a hybrid model combining Graph Neural Networks (GNNs) with a novel Series-Core Fusion (SC-Fusion) mechanism for urban traffic prediction. By leveraging GNNs for spatial modeling and SC-Fusion for efficient temporal dependency capture, the model effectively addresses complex spatio-temporal dynamics in traffic systems. Evaluated on six widely used traffic datasets-METR-LA, PEMS-BAY, PEMS03, PEMS04, PEMS07, and PEMS08-Starformer demonstrates consistent and robust performance across diverse traffic conditions and regions. The results highlight its ability to model both short-term and long-term dependencies, making it well-suited for real-world applications. These findings emphasize the potential of integrating advanced neural network architectures for intelligent traffic management, contributing to smarter, more sustainable urban transportation systems.
AB - This study introduces Starformer, a hybrid model combining Graph Neural Networks (GNNs) with a novel Series-Core Fusion (SC-Fusion) mechanism for urban traffic prediction. By leveraging GNNs for spatial modeling and SC-Fusion for efficient temporal dependency capture, the model effectively addresses complex spatio-temporal dynamics in traffic systems. Evaluated on six widely used traffic datasets-METR-LA, PEMS-BAY, PEMS03, PEMS04, PEMS07, and PEMS08-Starformer demonstrates consistent and robust performance across diverse traffic conditions and regions. The results highlight its ability to model both short-term and long-term dependencies, making it well-suited for real-world applications. These findings emphasize the potential of integrating advanced neural network architectures for intelligent traffic management, contributing to smarter, more sustainable urban transportation systems.
KW - Graph Neural Networks
KW - Series-Core Fusion
KW - Spatio-temporal Embeddings
KW - Traffic Prediction
UR - https://www.scopus.com/pages/publications/105009087542
U2 - 10.1109/ICAACE65325.2025.11019045
DO - 10.1109/ICAACE65325.2025.11019045
M3 - Conference Proceeding
AN - SCOPUS:105009087542
T3 - 2025 8th International Conference on Advanced Algorithms and Control Engineering, ICAACE 2025
SP - 1850
EP - 1854
BT - 2025 8th International Conference on Advanced Algorithms and Control Engineering, ICAACE 2025
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Conference on Advanced Algorithms and Control Engineering, ICAACE 2025
Y2 - 21 March 2025 through 23 March 2025
ER -
