Sanjeev Kadam, Anshul Agrawal, Aryan Bajaj, R. Agarwal, Rameesha Kalra, J. Shah
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Predicting Crude Oil Future Price Using Traditional and Artificial Intelligence-Based Model: Comparative Analysis
Crude oil is an imperative energy source for the global economy. The future value of crude oil is challenging to anticipate due to its nonstationarity in nature. The focus of this research is to appraise the explosive behavior of crude oil during 2007–2022, including the most recent influential crisis COVID-19 pandemic, to forecast its prices. The crude oil price forecasts by the traditional econometric ARIMA model were compared with modern Artificial Intelligence (AI)-based Long Short-Term Memory Networks (ALSTM). Root mean square error (RMSE) and mean average percent error (MAPE) values have been used to evaluate the accuracy of such approaches. The results showed that the ALSTM model performs better than the traditional econometric ARIMA forecast model while predicting crude oil opening price on the next working day. Crude oil investors can effectively use this as an intraday trading model and more accurately predict the next working day opening price.
期刊介绍:
Journal of International Commerce, Economics and Policy (JICEP) is a peer-reviewed journal that seeks to publish high-quality research papers that explore important dimensions of the global economic system (including trade, finance, investment and labor flows). JICEP is particularly interested in potentially influential research that is analytical or empirical but with heavy emphasis on international dimensions of economics, business and related public policy. Papers must aim to be thought-provoking and combine rigor with readability so as to be of interest to both researchers as well as policymakers. JICEP is not region-specific and especially welcomes research exploring the growing economic interdependence between countries and regions.