Brent oil price prediction using bi-lstm network

Abstract

Brent oil price fluctuates continuously causing instability in the econ-omy. 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 pri ces usual l y fluct uat e over t i me as a t i me seri es 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.