Analytical Study of Air Traffic Using ARFIMA Time Series Models

While time series forecasting techniques have been widely developed, the self-similar structure of data has not been adequately addressed. This research focuses on investigating self-similar structures in real-time air traffic data from Air India and Indigo's scheduled domestic flights, aiming to develop a suitable forecasting model for self-similar time series. Self-similarity has proven valuable, particularly in processes like ARFIMA, long-range dependence, and the Hurst parameter. This study explores the current understanding of self-similarity, its concepts, definitions, and applications, offering a roadmap for future research. The book consolidates past works on air traffic modeling using methods such as Box-Jenkins, Exponential Smoothing, and Artificial Neural Networks. It aims to present a comprehensive overview of time series forecasting developments, focusing on air traffic modeling, long-range dependence through self-similarity, and fitting ARFIMA to identify the most effective forecasting model.

Autorentext
Dr. D. Manohar, Associate Professor at ANURAG University, has over 17 years of teaching experience. He has published 17 research papers, authored two patents, and contributed to five international book chapters. Recognized for his excellence, he holds national and state-level awards and is a life member of SDS and ISPS.