PREDICTIVE ANALYTICS FOR CRUDE OIL PRICE USING RNN-LSTM NEURAL NETWORK
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Abstract
Prediction of future crude oil price is considered a significant challenge due to the extremely complex, chaotic, and dynamic nature of the market and stakeholder’s perception. The crude oil price changes every minute, and millions of shares ownerships are traded every day. The market price for commodity such as crude oil is influenced by many factors including news, supply-and-demand gap, labor costs, amount of remaining resources, as well as stakeholders’ perception. Therefore, various indicators for technical analysis have been utilized for the purpose of predicting the future crude oil price. Recently, many researchers have turned to machine learning approached to cater to this problem. This study demonstrated the use of RNN-LSTM networks for predicting the crude oil price based on historical data alongside other technical analysis indicators. This study aims to certify the capability of a prediction model built based on the RNN-LSTM network to predict the future price of crude oil. The developed model is trained and evaluated against accuracy matrices to assess the capability of the network to provide an improvement of the accuracy of crude oil price prediction as compared to other strategies. The result obtained from the model shows a promising prediction capability of the RNN-LSTM algorithm for predicting crude oil price movement.
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