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Brent Oil Price Prediction Using Bi-LSTM Network

Anh H. Vo1, Trang Nguyen2, Tuong Le1,3,*

1 Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam
2 Faculty of Information Technology, Ho Chi Minh City Open University, Ho Chi Minh City, Vietnam
3 Informetrics Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam

* Corresponding Author: Tuong Le. Email: email

Intelligent Automation & Soft Computing 2020, 26(6), 1307-1317. https://doi.org/10.32604/iasc.2020.013189

Abstract

Brent oil price fluctuates continuously causing instability in the economy. Therefore, it is essential to accurately predict the trend of oil prices, as it helps to improve profits for investors and benefits the community at large. Oil prices usually fluctuate over time as a time series and as such several sequence-based models can be used to predict them. Hence, this study proposes an efficient model named BOP-BL based on Bidirectional Long Short-Term Memory (Bi-LSTM) for oil price prediction. The proposed framework consists of two modules as follows: The first module has three Bi-LSTM layers which help learning useful information features in both forward and backward directions. The last fully connected layer is utilized in the second module to predict the oil price using important features extracted from the previous module. Finally, empirical experiments are conducted and performed on the Brent Oil Price (BOP) dataset to evaluate the prediction performance in terms of several common error metrics such as Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) among BOP-BL and three state-of-the-art models (for time series forecasting) including Long Short-Term Memory (LSTM), the combination of Convolutional Neural Network and LSTM (CNN-LSTM), and the combination of CNN and Bi-LSTM (CNN-Bi-LSTM). The experimental results demonstrate that the BOP-BL model outperforms state-of-the-art methods for predicting Brent oil price on the BOP dataset.

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Cite This Article

A. H. Vo, T. Nguyen and T. Le, "Brent oil price prediction using bi-lstm network," Intelligent Automation & Soft Computing, vol. 26, no.6, pp. 1307–1317, 2020.

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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