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Analyzing the robustness of ARIMA and neural networks as a predictive model of crude oil prices

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  • Sudhi SHARMA

    (Credit Guarantee Fund Trust for Micro and Small Enterprises (CGTMSE), Mumbai, India)

  • Miklesh YADAV

    (FIIB, Business School, New Delhi, India)

Abstract

The paper is focusing in analyzing the robustness of the Auto Regressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANNs) as a predictive model in forecasting the crude oil price. The paper has identified stochastic trend in the daily time series data starting from (03.01.2011 to 11.10.2019). The time considered in the study is subject to high volatility, which makes this paper unique from the current stock of knowledge. During this time frame it has been identified that there is no structural break. The empirical analysis furnishes that the ARIMA is the best suited model. The decision criterion for the selection of the best suited model depends on ME, RMSE, MAE and MASE. From the results of the criterion it has found that both the models are providing almost closed results but again ARIMA is the best suited model for the current data set.

Suggested Citation

  • Sudhi SHARMA & Miklesh YADAV, 2020. "Analyzing the robustness of ARIMA and neural networks as a predictive model of crude oil prices," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(2(623), S), pages 289-300, Summer.
    • Handle: RePEc:agr:journl:v:2(623):y:2020:i:2(623):p:289-300
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    References listed on IDEAS

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    Keywords

    ARIMA; ANNs; Crude-Oil.;
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