TailBooster：具操作有效性確保之雙層生成式極端值增強框架

摘要

航空運輸中的極端事件（例如嚴重抵達延誤與異常飛行時間）會引發連鎖式網絡中斷，造成可觀的營運、經濟與安全成本。此類事件在歷史紀錄中十分罕見，導致機器學習模型缺乏足夠的訓練訊號。合成數據增強提供了一個原理上可行的解決方案，但傳統生成式模型對分佈尾端的表徵不足，且無法保證不會產生營運上不可行的實例，例如短飛行時間搭配長飛行距離的組合。目前沒有任何既有方法能同時解決混合型表格紀錄的這兩項限制。我們提出 TailBooster，這是一個雙層生成式框架，結合生成式建模與兩個異常偵測層。統計層透過四分位距萃取極端值，為專用的生成式模型（此處為表格變分自編碼器）提供集中於分佈尾部的訓練訊號；深度學習層則套用基於自編碼器的清理機制，丟棄違反自歷史資料習得之營運包絡的合成紀錄。該框架以美國航班紀錄進行評估，涵蓋五個面向：多樣性、統計相似度、真實度、營運有效性與效用，其中後兩者為主要的改進目標。資料驅動的清理機制顯著提升了營運有效性，而針對性增強則強化了極端事件預測的效用。相較於傳統合成數據，在六種迴歸演算法上，使用該框架產製的紀錄進行訓練，可將極端飛行時間預測的平均絕對誤差降低 47–49%，極端抵達延誤預測則降低 29–57%；當以合成極端值擴充真實紀錄時，亦可獲得相當的增益。由於 TailBooster 完全以資料驅動且與模型無關，因此可延伸至極端事件預測至關重要、且缺乏領域特定規則的應用領域。

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.