Accurate Price prediction by Double Deep Q-Network


  • Mohammd Reza Feizi Derakhshi University of Tabriz, Iran
  • Bahram Lotfimanesh University of Tabriz, Iran
  • Omid Amani University College of Nabi Akram, Tabriz, Iran



Deep Reinforcement Learning, DDQN, LSTM, CNN, Prediction, Stock, Stock Price, Neural Network


For more than several decades, time series data have been in the center of attention for scholars to predict the future prices of the markets, the most fundamental and challenging of which has been the prediction of the price of the stock market. It is of great importance to note that the algorithms with the fewest errors in price predictions are more applicable. There have been more methods suggested for price prediction in the stock markets: time series data analysis, mathematical and statistical analysis, signal processing, pattern recognition and machine learning. One of the demerits of the aforementioned methods is failing to recognize sudden change of prices, in this regard, experiencing more errors is the consequence of such demerit. In this regard, to have the error solved, the DDQN algorithm, consisting of deep neural networks which includes LSTM-CNN layers, has been employed. Confronting price fluctuations, the agent has the privilege of having better performance by employing the advantages of LSTM-CNN layers. In this research, the algorithm has been carried out over Iranian Gold Market, including six various types of Gold, from 2009 to 2020. The results reveal the point that the given method is more precise in comparison with other suggested methods confronting sudden changes in prices.


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How to Cite

Feizi Derakhshi, M. R., Lotfimanesh, B., & Amani, O. (2024). Accurate Price prediction by Double Deep Q-Network. Inteligencia Artificial, 27(74), 12–21.