Construction of Advanced Machine Learning Models for Air Traffic - Couverture souple

Mounika, Panjala; Bhatracharyulu, Nallani Chakravarthula

Couverture souple

ISBN 10 : 6208436486 ISBN 13 : 9786208436483

Editeur : LAP LAMBERT Academic Publishing, 2025

Synopsis

This book explores the application of various time series and machine learning techniques to model and forecast domestic airline traffic. It provides a comprehensive study of traditional and modern predictive approaches. It presents an extensive literature review on airline traffic modeling, covering traditional time series methods(Holt’s Winter, ARIMA, SARIMA) alongside advanced machine learning techniques(FFNN, MLP, LSTM). A comparative analysis of these methods, highlighting their strengths and limitations, is also included. Further, it explores the Bayesian estimation of SARIMA model parameters. The estimated parameters and predictions are compared with the traditional maximum likelihood approach. It extends the research by introducing mixture models, hybrid approaches, and simple averaging techniques to enhance predictive accuracy. The effectiveness of these models is evaluated through comparative analysis.

Détails de l'édition

• Éditeur: LAP LAMBERT Academic Publishing
• Date d'édition: 2025
• Langue: anglais
• ISBN 10: 6208436486
• ISBN 13: 9786208436483
• Reliure: Broché
• Nombre de pages: 144
• Coordonnées du fabricant: non disponible
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