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本文提出一种融合 Temporal Fusion Transformer 与 N-BEATS 的注意力混合框架，用于统一多时间尺度的光伏功率预测，在 DKASC 真实数据集上相比最佳基线 TFT 实现 35–40% 的 RMSE 降低。
This paper presents a schedule-informed Temporal Fusion Transformer framework for forecasting hourly airport security-checkpoint throughput, using flight schedules converted into temporally aligned screening-load signals. The model achieves lower prediction errors compared to RNN and LSTM baselines on Atlanta airport data.
Proposes Hierarchical Temporal Fusion (HTF), an extension of the Temporal Fusion Transformer that integrates a coherence-aware loss function to ensure forecasts are consistent across hierarchical levels, achieving improved accuracy and coherence on benchmark datasets.
This paper proposes a probabilistic framework for Alzheimer's disease progression forecasting that combines ordinal diagnosis prediction, multi-horizon trajectory generation, and decomposed uncertainty estimation using a Temporal Fusion Transformer encoder and an autoregressive Mixture Density Network. The model outperforms baselines on ADNI data, achieving near-nominal 90% credible interval coverage with clinically meaningful uncertainty signals.