Zeng Xiaohua, Liang Changzhou, Yang Qian, Wang Fei, Cai Jieping
School of Economics and Trade, Guangzhou Xinhua University, Dongguan, China.
PLoS One. 2025 Jan 14;20(1):e0310296. doi: 10.1371/journal.pone.0310296. eCollection 2025.
Stock price prediction is a challenging research domain. The long short-term memory neural network (LSTM) widely employed in stock price prediction due to its ability to address long-term dependence and transmission of historical time signals in time series data. However, manual tuning of LSTM parameters significantly impacts model performance. PSO-LSTM model leveraging PSO's efficient swarm intelligence and strong optimization capabilities is proposed in this article. The experimental results on six global stock indices demonstrate that PSO-LSTM effectively fits real data, achieving high prediction accuracy. Moreover, increasing PSO iterations lead to gradual loss reduction, which indicates PSO-LSTM's good convergence. Comparative analysis with seven other machine learning algorithms confirms the superior performance of PSO-LSTM. Furthermore, the impact of different retrospective periods on prediction accuracy and finding consistent results across varying time spans are. Conducted in the experiments.
股票价格预测是一个具有挑战性的研究领域。长短期记忆神经网络（LSTM）因其能够处理时间序列数据中的长期依赖性和历史时间信号的传递，而被广泛应用于股票价格预测。然而，LSTM参数的手动调整对模型性能有显著影响。本文提出了利用粒子群优化算法（PSO）的高效群体智能和强大优化能力的PSO-LSTM模型。对六个全球股票指数的实验结果表明，PSO-LSTM能够有效地拟合实际数据，实现较高的预测精度。此外，增加PSO迭代次数会导致损失逐渐减少，这表明PSO-LSTM具有良好的收敛性。与其他七种机器学习算法的对比分析证实了PSO-LSTM的优越性能。此外，还进行了不同回顾期对预测精度的影响以及在不同时间跨度上寻找一致结果的实验。
