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Predicting Indian basket crude prices through machine learning models - a comparative approach

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  • Pradip Kumar Mitra
  • Charu Banga

Abstract

Forecasting crude price can bring some stability in the decision making process for the firms dealing with it. Crude oil is a very volatile commodity so only linear time series modelling is not sufficient to predict its price. A nonlinear model like an artificial neural network is a better choice. The paper tries to test the prediction accuracy of a conventional neural network model and deep learning model using monthly data of Indian basket price of crude oil for 18 years. A simple MLP neural network model and a deep learning model of long short-term memory are used in the present study to find accuracies in predicting the crude price. The paper finds that a simple MLP model can provide better forecasting accuracy compared to a complicated LSTM model.

Suggested Citation

  • Pradip Kumar Mitra & Charu Banga, 2019. "Predicting Indian basket crude prices through machine learning models - a comparative approach," International Journal of Business Forecasting and Marketing Intelligence, Inderscience Enterprises Ltd, vol. 5(3), pages 249-266.
    • Handle: RePEc:ids:ijbfmi:v:5:y:2019:i:3:p:249-266
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