Time series analysis of airline ticket prices

Abstract

Air travel has become more accessible than ever, and the volatility of airline ticket prices presents a challenge and opportunity for both consumers and industry players. This dissertation delves into the complexities surrounding airline ticket pricing, specifically targeting one of Europe’s leading low-cost carriers, and investigates approaches for analysing and predicting fare prices. By analysing time series price data from October to December 2023, this study investigates the potential of two predictive models – the Holt-Winters method and Seasonal Autoregressive Moving Average method to predict price fluctuations. This feasibility and comparative study seeks to identify which model, if at all, is more adequate for this complex task. This study also features various interesting analysis over 39 flights on 3 separate dates, and certain hypotheses will be tested. The implications of this research may prove to be significant for the airline industry, travel agencies, and consumers, potentially informing pricing strategies, aiding in revenue management, and empowering travelers with knowledge to make more informed purchasing decisions.