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Li, C., Zhang, X., Qaosar, M., Ahmed, S., Alam, K.M.R. and Morimoto, Y. (2019) Multi-Factor Based Stock Price Prediction Using Hybrid Neural Networks with Attention Mechanism. 2019 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference Cloud and Big Data Computing, International Conference on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), Fukuoka, 5-8 August 2019, 961-966.
https://doi.org/10.1109/dasc/picom/cbdcom/cyberscitech.2019.00176

has been cited by the following article:

• TITLE: Gold Price Prediction Based on LSTM-Attention Combined Model

  AUTHORS: Junping Wu, Jihua Liu

  KEYWORDS: Gold, LSTM, Price Prediction, Convolutional Self-Attention (CSA) Network

  JOURNAL NAME: Open Access Library Journal, Vol.13 No.1, January 14, 2026

  ABSTRACT: This paper explores effective methods for predicting gold prices, proposing three modeling strategies: a standalone Long Short-Term Memory (LSTM) network, a Convolutional Self-Attention (CSA) Network, and a combination model of both. Through empirical analysis, we systematically compare the performance of these three models in gold price forecasting, with a focus on evaluating their predictive accuracy. The experimental results indicate that the LSTM-Attention combination model significantly outperforms the standalone LSTM and Convolutional Self-Attention (CSA) Network in terms of prediction accuracy, demonstrating a more comprehensive ability to capture the dynamic features of price fluctuations. This model not only showcases the effective integration of LSTM and attention mechanisms in time series forecasting but also provides a practical tool for financial decision-making, offering valuable insights for investors in a volatile market environment.