Machine Learning-Based Forecasting of Qatar's LNG Export Volumes Under Market Volatility

Madhava Miglani

Abstract: The uses of Liquified Natural Gases (LNG) are vast, from generating electricity at huge scales for a town?s grid to being burnt using a gas stove to cook. LNG is just Natural Gas (NG) cooled down to -162?C (-260?F) to turn it into liquid form to make it easier to transport as, the volume has decreased (meaning more can be transported at once) and that it is in a liquid form which is more stable making it less prone to major disasters. Qatar is a major exporter of LNG, in fact it is the third largest producer of it after the US and Australia. Qatar produces 20% of the global supply, this means many countries rely on Qatar for their LNG. The main goal of this study is to build statistical models which can accurately analyse and predict Qatar?s LNG exports based on previous data and benchmark variables. The data used in this study consist of Qatar's monthly export values for the period 2019-2024, the export volume and values in terms of the mass exported from Qatar for the same period, and finally the Henry Hub and Asia LNG prices as gas price benchmarks. The statistical and machine learning models used in this study are the ARIMA and the Random Forest models. The results show that while ARIMA provides a useful baseline prediction, the Random Forest model performs better because it can include more variables such as global gas prices. These findings suggest that machine learning methods can improve forecasting accuracy for LNG export markets.

Keywords: Qatar, LNG exports, machine learning, econometric models

How to Cite?: Madhava Miglani, "Machine Learning-Based Forecasting of Qatar's LNG Export Volumes Under Market Volatility", Volume 15 Issue 3, March 2026, International Journal of Science and Research (IJSR), Pages: 865-871, https://www.ijsr.net/getabstract.php?paperid=SR26307185914, DOI: https://dx.dx.doi.org/10.21275/SR26307185914

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