Predicting Crude Oil Future Price Using Traditional and Artificial Intelligence-Based Model: Comparative Analysis
- Title
- Predicting Crude Oil Future Price Using Traditional and Artificial Intelligence-Based Model: Comparative Analysis
- Creator
- Kadam S.; Agrawal A.; Bajaj A.; Agarwal R.; Kalra R.; Shah J.
- Description
- 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 20072022, 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. 2023 World Scientific Publishing Co. Pte Ltd. All rights reserved.
- Source
- Journal of International Commerce, Economics and Policy, Vol-14, No. 3
- Date
- 2023-01-01
- Publisher
- World Scientific
- Subject
- ALSTM; ARIMA; Artificial intelligence; crude oil; forecast; RNN-LSTM
- Coverage
- Kadam S., Symbiosis Institute of Business Management Pune Symbiosis International (Deemed University), Maharashtra, India; Agrawal A., Jaypee Institute of Information Technology, Noida, India; Bajaj A., GISMA School of Business University of Applied Sciences, Potsdam, Germany; Agarwal R., University School of Business Chandigarh University, Punjab, Mohali, 140413, India; Kalra R., School of Business Management CHRIST (Deemed to be University), Bangalore, India; Shah J., Amity Business School Amity University, Panvel, Mumbai, India
- Rights
- Restricted Access
- Relation
- ISSN: 17939933
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Kadam S.; Agrawal A.; Bajaj A.; Agarwal R.; Kalra R.; Shah J., “Predicting Crude Oil Future Price Using Traditional and Artificial Intelligence-Based Model: Comparative Analysis,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 26, 2025, https://archives.christuniversity.in/items/show/14053.