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

Pradip Kumar Mitra and Charu Banga

International Journal of Business Forecasting and Marketing Intelligence, 2019, vol. 5, issue 3, 249-266

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.

Keywords: crude oil price; forecasting; machine learning models; multilayer perceptron; MLP; neural network; long short-term memory; LSTM. (search for similar items in EconPapers)
Date: 2019
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