Authors:
E. S. Vimal Kumar, K. Chitra, V. M. Harilakshmi

Addresses:
Department of Computer Applications, Dayananda Sagar Academy of Technology and Management, Bengaluru, Karnataka, India.

Abstract:

The Current study uses the ARIMA model to analyze air travel data, forecast seasonality and trend, and determine the long-run trend in dynamic air transportation industry behaviour. ARIMA models are well-suited for analysing non-stationary data, which consists of autoregressive and moving average components. The methodology process includes testing the data's statistical properties, selecting appropriate parameters, fitting the model to the available data, and forecasting. It will provide accurate forecasts to stakeholders in the aviation industry, enabling effective resource planning and informed strategic decision-making. It will enhance foresight capability, leading to more active management practices and effective operations. The research aims to apply advanced statistical methods in real-world practice, demonstrating the potential of ARIMA models to forecast and analyse complex time series data, facilitate operational improvement, and contribute to the literature on time series analysis. It utilises three parameters, p, d, and q, to determine the optimal value of the model. ARIMA methods analyse air travel data, provide a clear indication of trends, and offer actionable insights for operational and strategic leadership in the air transportation industry.

Keywords: ARIMA Parameters; ARIMA Techniques; Air Travel Data; Forecast Seasonality; Determine Long-Run; Dynamic Air Transportation; Autoregression and Integration; Moving Average.

Received on: 22/08/2024, Revised on: 28/10/2024, Accepted on: 05/12/2024, Published on: 12/06/2025

DOI: 10.69888/FTSESS.2025.000464

FMDB Transactions on Sustainable Environmental Sciences, 2025 Vol. 2 No. 2, Pages: 60-68

• Views : 39
• Downloads : 6

Download PDF