SAGMTL decomposes dynamic sparse OD demand prediction into joint structural state modeling and flow intensity estimation via node-edge collaborative graph representations.
Graph neural network for traffic forecasting: A survey,
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Mixture-of-experts fusing multiple pretrained forecasters achieves strongest performance on influenza time series, with pretraining gains largest at longer horizons when domain-aligned and LLM methods underperforming.
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Structure-Aware Graph Multi-Task Learning for Dynamic Sparse OD Demand Prediction
SAGMTL decomposes dynamic sparse OD demand prediction into joint structural state modeling and flow intensity estimation via node-edge collaborative graph representations.
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Understanding Key Features of Time Series Foundation Models from Epidemic Forecasting
Mixture-of-experts fusing multiple pretrained forecasters achieves strongest performance on influenza time series, with pretraining gains largest at longer horizons when domain-aligned and LLM methods underperforming.