Abstract

A popular univariate time-series forecasting model for short-term fluctuations in air traffic flow is the SARIMA model, and its parameters are often estimated using the method of maximum likelihood. This paper focused on estimation of its parameters using the Bayesian approach. The data used is the number of domestic air passengers travelling through Air India and Spice Jet airlines. The forecasts from the Bayesian parameter estimation performed better than those using maximum likelihood estimation.

Author: Mounika Panjala, Ranjankumar Sahoo, Bhatracharyulu N. Ch

Received on: August, 2024

Accepted on: December, 2024