Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Construction of Advanced Machine Learning Models for Air Traffic

Construction of Advanced Machine Learning Models for Air Traffic

Mounika, Panjala
Bhatracharyulu, Nallani Chakravarthula

Paperback

Probability & Statistics

ISBN10: 6208436486
ISBN13: 9786208436483
Publisher: LAP Lambert Academic Publishing
Published: Mar 18 2025
Pages: 144
Weight: 0.44
Height: 0.34 Width: 6.00 Depth: 9.00
Language: English
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.

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