ISBN: 9786208436483 - construction of advanced machine learning models for air traffic (11 risultati)

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Condizione: New. In English.

Condizione: New.

Taschenbuch. Condizione: Neu. Construction of Advanced Machine Learning Models for Air Traffic | Panjala Mounika (u. a.) | Taschenbuch | Englisch | 2025 | LAP LAMBERT Academic Publishing | EAN 9786208436483 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.…

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 144 pp. Englisch.

Condizione: New. Print on Demand.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.…

Condizione: New. PRINT ON DEMAND.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 144 pp. Englisch.…