Abstract:
Flight delays cost the aviation industry over $28 billion annually, yet most systems remain reactive, and existing research often fails to translate predictive models into real-world operational tools. Skylytics addresses this by providing a full-stack, proactive flight delay prediction system designed for both operational managers and passengers. It uses a hybrid stacking ensemble combining XGBoost and Bidirectional LSTM models, integrated through a Logistic Regression meta-learner, leveraging their complementary strengths. The system is trained on around 14 million flight records (2024–2025), enriched with weather data from Open-Meteo and live air traffic data from OpenSky Network. Multiple models were evaluated under a leakage-free setup, with XGBoost achieving a ROC-AUC of 0.7935, while the BiLSTM contributed significantly to the ensemble. The system is deployed via a FastAPI backend, a Next.js operations dashboard, and a SwiftUI iOS app for passenger tracking. It also includes. SHAP-based interpretability and an AI assistant for natural-language insights. Overall, Skylytics bridges the gap between academic research and production systems, enabling proactive decision-making and improving reliability for both airlines and passengers.
