Abstract—Crude oil plays a vital role in the global economy and forecasting crude oil prices is crucial for both government and private sectors. However, the crude oil price is high volatility, influenced by various factors and challenging to predict. Thus, various machine learning techniques have been proposed to predict crude oil prices for decades. In this study, we propose an Artificial Neural Network (ANN) with different combinations of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) to improve the trend forecasting of crude oil prices for better trading signals compared to traditional strategies. As the crude oil price is a time series data, it is appropriate to apply CNN and LSTM for forecasting. The concept of our model is that CNN could detect features or patterns in different locations of time series data, while LSTM could maintain both short-term and long-term memory along with time series data. The collaboration of their abilities could help the neural network model understand complex relationships of historical data and trends of crude oil prices. Our study found that the combination of CNN and LSTM could significantly enhance trading performance in the long run.  
 

Index Terms—crude oil trading, machine learning, deep learning, trading signal, technical analysis, artificial intelligent 

Cite: Wisaroot Lertthaweedech, Pittipol Kantavat, and Boonserm Kijsirikul, "Effective Crude Oil Trading Techniques Using Long Short-Term Memory and Convolution Neural Networks," Journal of Advances in Information Technology, Vol. 13, No. 6, pp. 645-651, December 2022.

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