Modeling the behavior of stock price data has always been one of the challengeous applications of Artificial Intelligence (AI) and Machine Learning (ML) due to its high complexity and dependence on various conditions. Recent studies show that this will be difficult to do with just one learning model. The problem can be more complex for companies of construction section, due to the dependency of their behavior on more conditions. This study aims to provide a hybrid model for improving the accuracy of prediction for stock price index of companies in construction section. The contribution of this paper can be considered as follows: First, a combination of several prediction models is used to predict stock price, so that learning models can cover each other's error. In this research, an ensemble model based on Artificial Neural Network (ANN), Gaussian Process Regression (GPR) and Classification and Regression Tree (CART) is presented for predicting stock price index. Second, the optimization technique is used to determine the effect of each learning model on the prediction result. For this purpose, first all three mentioned algorithms process the data simultaneously and perform the prediction operation. Then, using the Cuckoo Search (CS) algorithm, the output weight of each algorithm is determined as a coefficient. Finally, using the ensemble technique, these results are combined and the final output is generated through weighted averaging on optimal coefficients. The results showed that using CS optimization in the proposed ensemble system is highly effective in reducing prediction error. Comparing the evaluation results of the proposed system with similar algorithms, indicates that our model is more accurate and can be useful for predicting stock price index in real-world scenarios.

翻译：股票价格数据的行为建模由于其高度复杂性和依赖多种条件，一直是人工智能和机器学习领域最具挑战性的应用之一。近期研究表明，仅使用单一学习模型难以实现这一目标。对于建筑行业公司而言，其行为模式对更多条件的依赖性使得问题更为复杂。本研究旨在提出一种混合模型，以提高建筑行业公司股票价格指数预测的准确性。本文的贡献如下：首先，采用多种预测模型的组合进行股票价格预测，使各学习模型能够相互弥补误差。本研究提出一种基于人工神经网络、高斯过程回归和分类回归树的集成模型用于股票价格指数预测。其次，采用优化技术确定各学习模型对预测结果的影响程度。为此，首先让上述三种算法同时处理数据并执行预测操作，然后利用布谷鸟搜索算法确定各算法输出的权重系数。最后，通过集成技术将这些结果进行组合，对最优系数进行加权平均生成最终输出。结果表明，在所提出的集成系统中使用CS优化在降低预测误差方面效果显著。将所提系统的评估结果与同类算法进行比较，显示本模型具有更高的准确性，可用于真实场景下的股票价格指数预测。