The prediction errors of the optimal parameters.

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.

股价预测是极具挑战性的研究领域。长短期记忆神经网络（LSTM）因具备处理时序数据中长期依赖关系与历史时间信号传递的能力，被广泛应用于股价预测任务。然而，手动调优LSTM的参数会对模型性能造成显著影响。本文提出了一种融合粒子群优化（Particle Swarm Optimization，PSO）高效群体智能与强大优化能力的PSO-LSTM模型。针对全球六大股票指数开展的实验结果表明，PSO-LSTM能够有效拟合真实市场数据，实现较高的预测精度。此外，随着PSO迭代次数的增加，模型损失值逐步降低，这表明PSO-LSTM具备良好的收敛性能。与其余七种机器学习算法的对比分析进一步验证了PSO-LSTM的优异性能。此外，实验还探究了不同回溯周期对预测精度的影响，并在不同时间跨度下均得到了一致的实验结果。