Since flight delay hurts passengers, airlines, and airports, its prediction
becomes crucial for the decision-making of all stakeholders in the aviation
industry and thus has been attempted by various previous research. However,
previous delay predictions are often categorical and at a highly aggregated
level. To improve that, this study proposes to apply the novel Temporal Fusion
Transformer model and predict numerical airport arrival delays at quarter hour
level for U.S. top 30 airports. Inputs to our model include airport demand and
capacity forecasts, historic airport operation efficiency information, airport
wind and visibility conditions, as well as enroute weather and traffic
conditions. The results show that our model achieves satisfactory performance
measured by small prediction errors on the test set. In addition, the
interpretability analysis of the model outputs identifies the important input
factors for delay prediction.

采用新型时间融合变压器模型，对美国前 30 个机场的 15 分钟级别的到达延误进行数值预测，并分析模型输出的重要输入因素。

基于时间融合变压器的机场延误预测

Airport Delay Prediction with Temporal Fusion Transformers

Prognosis of the reactor accident is a crucial way to ensure appropriate
strategies are adopted to avoid radioactive releases. However, there is very
limited research in the field of nuclear industry. In this paper, we propose a
method for accident prognosis based on the Temporal Fusion Transformer (TFT)
model with multi-headed self-attention and gating mechanisms. The method
utilizes multiple covariates to improve prediction accuracy on the one hand,
and quantile regression methods for uncertainty assessment on the other. The
method proposed in this paper is applied to the prognosis after loss of coolant
accidents (LOCAs) in HPR1000 reactor. Extensive experimental results show that
the method surpasses novel deep learning-based prediction methods in terms of
prediction accuracy and confidence. Furthermore, the interference experiments
with different signal-to-noise ratios and the ablation experiments for static
covariates further illustrate that the robustness comes from the ability to
extract the features of static and historical covariates. In summary, this work
for the first time applies the novel composite deep learning model TFT to the
prognosis of key parameters after a reactor accident, and makes a positive
contribution to the establishment of a more intelligent and staff-light
maintenance method for reactor systems.

本文提出了一种基于多头自注意和门控机制的时间融合 Transformer 模型的事故预测方法，利用多个协变量来提高预测精度，并采用分位数回归方法进行不确定性评估，该方法首次将 TFT 应用于反应堆事故预测中，相对于新型基于深度学习的预测方法，在预测精度和置信度上均有所提高，同时还可以提取静态和历史协变量特征，为建立更智能和更轻松的反应堆系统维护方法做出积极贡献。