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.