A transformer encoder architecture for node-level time series forecasting
Experimental results show that the proposed model outperforms traditional RNN and LSTM models across various tasks, indicating promising potential for practical applications.
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Experimental results show that the proposed model outperforms traditional RNN and LSTM models across various tasks, indicating promising potential for practical applications.
This study proposes a structurally simple, low-node-density disjunctive graph model, then performs feature extraction using Graph Neural Networks (GNNs), and finally optimize convergence using the Migrating Birds Optimization—an intelligent optimization algorithm proven effective for scheduling decision problems.
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