An Information Fusion Technique for Prognosticating Future Air Passenger Trends

Abstract

The aviation industry is constantly changing and to keep up with the trends of air passengers we need predictive models. In this paper, we explore the use of Information Fusion methodologies and classical time series techniques to forecast how many passengers will be traveling by air. Predicting passenger demands is a task, due to various factors that influence travel patterns. The existing models often struggle to capture the dynamics in this field so it's crucial to develop accurate forecasting methods. By leveraging information fusion techniques like smoothing and Autoregressive Integrated Moving Average (ARIMA) our research creates models based on historical data of air passenger volumes. These techniques combine machine learning algorithms and time series analysis to identify dependencies and patterns in the dataset. Through evaluations and comparative analyses, our proposed models demonstrate promising capabilities in forecasting future air passenger volumes. Proof-of-concept experiments based on 5-fold cross-validation demonstrate the efficacy of the proposed approach in capturing underlying trends and seasonality within the dataset.