Petroleum Price Prediction with CNN-LSTM and CNN-GRU Using Skip-Connection

Abstract

Crude oil plays an important role in the global economy, as it contributes one-third of the energy consumption worldwide. However, despite its importance in policymaking and economic development, forecasting its price is still challenging due to its complexity and irregular price trends. Although a significant amount of research has been conducted to improve forecasting using external factors as well as machine-learning and deep-learning models, only a few studies have used hybrid models to improve prediction accuracy. In this study, we propose a novel hybrid model that captures the finer details and interconnections between multivariate factors to improve the accuracy of petroleum oil price prediction. Our proposed hybrid model integrates a convolutional neural network and a recurrent neural network with skip connections and is trained using petroleum oil prices and external data directly accessible from the official website of South Korea’s national oil corporation and the official Yahoo Finance site. We compare the performance of our univariate and multivariate models in terms of the Pearson correlation, mean absolute error, mean squared error, root mean squared error, and R squared (R2) evaluation metrics. Our proposed models exhibited significantly better performance than the existing models based on long short-term memory and gated recurrent units, showing correlations of 0.985 and 0.988, respectively, for 10-day price predictions and obtaining better results for longer prediction periods when compared with other deep-learning models. We validated that our proposed model with skip connections outperforms the benchmark models and showed that the convolutional neural network using gated recurrent units with skip connections is superior to the compared models. The findings suggest that, to some extent, relying on a single source of data is ineffective in predicting long-term changes in oil prices, and thus, to develop a better prediction model based on time-series based data, it is necessary to take a multivariate approach and develop an efficient computational model with skip connections.