Crude Oil Price Prediction Using Artificial Neural Networks: A Time Series Modeling Approach

Abstract

Crude oil is the primary fuel and its price has a direct impact on oil exploration, exploitation, and other activities, as well as on the environment and on our economy. Hence, it is among the world's most abundant resources today. Crude oil is essential to the functioning of every modern economy. Considering crude oil's high volume of trading, speculators, analysts, and economists have vested interest in correctly projecting the commodity's future spot price. However, predicting such an apparently uncertain, economic environment is one of the primary challenges of econometric models. Oil price forecasts based on fundamental, technical, and time series analysis have been met with mixed success. This highlights the requirement for more refined methods of predicting future crude oil prices.

This study uses a neural network to objectively foretell the price of crude oil. There are thirteen predictors and one dependent variable in this study. In this study Neural Networks (NN) and time series techniques are used to forecast time series data and out of these NN have been found to be the most proficient method. The split in the data is 70-30. 30% of the data is being used to verify the accuracy of the network's predictions. Speculating on the future cost of crude oil, requires the employment of feed forward and back propagation algorithms. Latest neural networks techniques are quite predictive as time series models are beaten by even the simplest of neural networks. The results of the investigation showed that the back propagation algorithm is superior in predicting the cost of crude oil. Hence, ANN can be used by financiers, and forecasters.

Keywords

artificial neural network RMSE RMSD MAE crude oil prediction

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