IDEAS home Printed from https://ideas.repec.org/a/wsi/jicepx/v14y2023i03ns179399332350014x.html
   My bibliography  Save this article

Predicting Crude Oil Future Price Using Traditional and Artificial Intelligence-Based Model: Comparative Analysis

Author

Listed:
  • Sanjeev Kadam

    (Symbiosis Institute of Business Management Pune, Symbiosis International (Deemed University), Maharashtra, India)

  • Anshul Agrawal

    (��Jaypee Institute of Information Technology, Noida, India)

  • Aryan Bajaj

    (��GISMA School of Business, University of Applied Sciences, Potsdam, Germany)

  • Rachit Agarwal

    (�University School of Business, Chandigarh University, Mohali, 140413, Punjab, India)

  • Rameesha Kalra

    (�School of Business Management, CHRIST (Deemed to be University), Bangalore, India)

  • Jaymin Shah

    (��Amity Business School, Amity University Mumbai, Panvel, India)

Abstract

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.

Suggested Citation

  • Sanjeev Kadam & Anshul Agrawal & Aryan Bajaj & Rachit Agarwal & Rameesha Kalra & Jaymin Shah, 2023. "Predicting Crude Oil Future Price Using Traditional and Artificial Intelligence-Based Model: Comparative Analysis," Journal of International Commerce, Economics and Policy (JICEP), World Scientific Publishing Co. Pte. Ltd., vol. 14(03), pages 1-15, October.
    • Handle: RePEc:wsi:jicepx:v:14:y:2023:i:03:n:s179399332350014x
    DOI: 10.1142/S179399332350014X
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S179399332350014X
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1142/S179399332350014X?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    Keywords

    Artificial intelligence; ALSTM; ARIMA; crude oil; forecast; RNN-LSTM;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • F47 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Forecasting and Simulation: Models and Applications

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wsi:jicepx:v:14:y:2023:i:03:n:s179399332350014x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/jicep/jicep.shtml .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.