pith:5RU3JDLQ
FlightSense: An End-to-End MLOps Platform for Real-Time Flight Delay Prediction via Rotation-Chain Propagation Features and Agentic Conversational AI
Tracking delays through aircraft rotation chains via tail numbers raises prediction AUC from 0.732 to 0.879 in a deployed real-time system.
arxiv:2605.07364 v2 · 2026-05-08 · cs.LG
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Claims
Version 2 introduces 11 delay propagation features derived from aircraft rotation chains via tail-number tracking, yielding the dominant performance gain (AUC 0.732 to 0.875) and surpassing the single-stage XGBoost baseline reported by Zhou (2025).
That tail-number tracking in the BTS data accurately reconstructs true aircraft rotation chains without missing connections, data errors, or selection bias that would make the derived propagation features non-generalizable beyond the 2018 training period.
FlightSense improves flight delay prediction to 0.879 AUC by adding aircraft rotation-chain propagation features and weather inputs to XGBoost, then deploys the model with a live conversational AI interface on AWS.
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| First computed | 2026-05-20T00:03:14.836879Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
ec69b48d70503624857e958e3053b004bb2374bd7e57b75eb9229f3f97d5beb6
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/5RU3JDLQKA3CJBL6SWHDAU5QAS \
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Canonical record JSON
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