School of Information Science and Engineering, Zaozhuang University, Zaozhuang, China.
Comput Intell Neurosci. 2019 Feb 5;2019:7198962. doi: 10.1155/2019/7198962. eCollection 2019.
Stock index prediction is considered as a difficult task in the past decade. In order to predict stock index accurately, this paper proposes a novel prediction method based on S-system model. Restricted gene expression programming (RGEP) is proposed to encode and optimize the structure of the S-system. A hybrid intelligent algorithm based on brain storm optimization (BSO) and particle swarm optimization (PSO) is proposed to optimize the parameters of the S-system model. Five real stock market prices such as Dow Jones Index, Hang Seng Index, NASDAQ Index, Shanghai Stock Exchange Composite Index, and SZSE Component Index are collected to validate the performance of our proposed method. Experiment results reveal that our method could perform better than deep recurrent neural network (DRNN), flexible neural tree (FNT), radial basis function (RBF), backpropagation (BP) neural network, and ARIMA for 1-week-ahead and 1-month-ahead stock prediction problems. And our proposed hybrid intelligent algorithm has faster convergence than PSO and BSO.
在过去的十年中，股票指数预测被认为是一项具有挑战性的任务。为了准确地预测股票指数，本文提出了一种基于 S 系统模型的新的预测方法。提出了受限基因表达编程（RGEP）来对 S 系统的结构进行编码和优化。提出了一种基于头脑风暴优化（BSO）和粒子群优化（PSO）的混合智能算法来优化 S 系统模型的参数。收集了五个真实的股票市场价格，如道琼斯指数、恒生指数、纳斯达克指数、上海证券交易所综合指数和深圳证券交易所成分指数，以验证我们提出的方法的性能。实验结果表明，我们的方法在一周和一个月的股票预测问题上，比深度递归神经网络（DRNN）、灵活神经网络树（FNT）、径向基函数（RBF）、反向传播（BP）神经网络和 ARIMA 表现更好。而且，我们提出的混合智能算法比 PSO 和 BSO 具有更快的收敛速度。
