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

Sudhi Sharma and Miklesh Yadav
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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

Theoretical and Applied Economics, 2020, vol. XXVII, issue 2(623), Summer, 289-300

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.

Keywords: ARIMA; ANNs; Crude-Oil. (search for similar items in EconPapers)
Date: 2020
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