Hyperparameter-optimized generative models augment scarce flight diversion records and substantially improve prediction accuracy over real data alone.
Numerically more stable computation of the p- values for the two-sample kolmogorov-smirnov test
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Synthetic flight data generated by TVAE and Gaussian Copula models supports flight delay prediction models with accuracy comparable to real data.
citing papers explorer
-
Generative Augmentation of Imbalanced Flight Records for Flight Diversion Prediction: A Multi-objective Optimisation Framework
Hyperparameter-optimized generative models augment scarce flight diversion records and substantially improve prediction accuracy over real data alone.
-
Synthetic Flight Data Generation Using Generative Models
Synthetic flight data generated by TVAE and Gaussian Copula models supports flight delay prediction models with accuracy comparable to real data.