Forecasting Models for Non-Scheduled Air Transportation: Statistical Analysis, Machine Learning Methods and Hybrid Forecasting Approaches

This book focuses on the development and application of modern forecasting models for non-scheduled passenger air transportation. Several quantitative approaches are analyzed, including regression models, ARIMA stochastic models, Support Vector Machine (SVM) methods, fuzzy forecasting models, and hybrid forecasting techniques. The research presented in this book aims to analyze the effectiveness of different forecasting approaches and to identify models that provide higher forecasting accuracy. The findings may be useful for researchers, aviation specialists, transport analysts, and students interested in aviation systems and forecasting methods.