TailBooster：一种带操作有效性保障的双层极值增强生成框架

摘要

航空运输中的极端事件，例如严重的到达延误和异常飞行时间，会引发级联性的网络中断，造成巨大的运行、经济和安全成本。此类事件在历史记录中较为罕见，导致机器学习模型可用的训练信号不足。合成数据增强提供了一种合理的解决方案，但传统生成模型对分布尾部的表征不足，且无法保证不产生运行上不可行的实例，例如短飞行时间与长飞行距离的组合。现有方法均无法同时解决混合类型表格数据中的这两个局限性。我们提出TailBooster，一个双层生成框架，将生成建模与两层异常检测相结合。统计层通过四分位距提取极端值，为专用生成模型（此处为表格变分自编码器）提供集中于尾部的训练信号。深度学习层随后应用基于自编码器的清洗，剔除违反从历史数据中学习的运行包络的合成记录。该框架在美国航班数据上从五个维度进行了评估：多样性、统计相似性、保真度、运行有效性和效用性，后两者为主要改进目标。数据驱动的清洗显著提高了运行有效性，而针对性增强则提升了极端事件预测的效用性。在六种回归算法中，相对于传统合成数据，基于该框架记录的训练在极端飞行时间预测上将平均绝对误差降低了47%–49%，在极端到达延误预测上降低了29%–57%；当真实记录与合成极端值相结合时，也获得了相当的增益。TailBooster完全基于数据驱动且与模型无关，可推广至极端事件预测至关重要且缺乏领域特定规则的其他领域。

English

Extreme events in air transport, such as severe arrival delays and abnormal air times, cause cascading network disruptions with substantial operational, economic, and safety costs. Such events are rare in historical records, leaving insufficient training signal for machine learning models. Synthetic data augmentation offers a principled solution, but conventional generative models under-represent distributional tails and give no guarantee against operationally infeasible instances, such as a short air time paired with a long flight distance. No existing approach addresses both limitations for mixed-type tabular records. We propose TailBooster, a dual-layer generative framework combining generative modelling with two anomaly detection layers. A statistical layer extracts extremes via the interquartile range, supplying tail-concentrated training signal to dedicated generative models, here a Tabular Variational Autoencoder. A deep learning layer then applies autoencoder-based cleaning, discarding synthetic records that violate the operational envelope learned from historical data. The framework was evaluated on US flight records across five dimensions: diversity, statistical similarity, fidelity, operational validity, and utility, the latter two being the primary improvement targets. Data-driven cleaning markedly improved operational validity, while targeted augmentation enhanced utility for extreme-event prediction. Across six regression algorithms, training on the framework's records reduced Mean Absolute Error by 47-49% on extreme air time and 29-57% on extreme arrival delay prediction relative to conventional synthetic data, with comparable gains when real records were enriched with synthetic extremes. Being fully data-driven and model-agnostic, TailBooster extends to domains where extreme-event prediction is critical and domain-specific rules are unavailable.