Improving Deep Learning Models by Bayesian Optimization to Predict Crude Oil Prices

We implement, optimize, and compare the performance of deep learning models in forecasting prices of crude oil markets, namely West Texas Intermediate (WTI) and Brent. We focus on deep learning models as these are state-of-the-art forecasting systems for complex and nonlinear time series. In this regard, we implement convolutional neural networks (CNNs), long short-term memory (LSTM), and gated recurrent units (GRUs). Classical recurrent neural networks (RNNs) are chosen as the baseline artificial neural networks. We contribute to the literature by examining the effect of fine-tuning of the parameters of the predictive systems by means of Bayesian optimization (BO) on their performance. Also, to check the robustness of the optimized models, they are trained and tested on daily, weekly, and monthly data. The assessment of forecasting performance is based on three different metrics including the root mean of squared errors (RMSE), mean absolute deviation (MAD), and mean absolute percentage error (MAPE). The simulation results show that the GRU-BO and RNN-BO are respectively the best systems to predict prices of BRENT and WTI. In addition, the simulation results show that BO enhances the accuracy of the predictive models. The results obtained would help oil producers, suppliers, traders, and investors to implement the appropriate prediction system for each market to improve accuracy and generate profits for each time horizon.