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因其高度复杂性及对多种条件的依赖性，始终是人工智能与机器学习领域具有挑战性的应用之一。近期研究表明，仅使用单一学习模型难以有效完成此任务。对于建筑行业的公司而言，由于其行为受更多条件制约，该问题可能更为复杂。本研究旨在提出一种混合模型，以提高建筑行业公司股价指数的预测精度。本文的贡献可归纳如下：首先，采用多种预测模型组合进行股价预测，使学习模型能够相互弥补误差。本研究提出了一种基于人工神经网络、高斯过程回归与分类回归树的集成模型，用于预测股价指数。其次，采用优化技术确定各学习模型对预测结果的影响权重。为此，首先令上述三种算法同时处理数据并执行预测操作，随后利用布谷鸟搜索算法确定各算法输出权重作为系数。最后，通过集成技术将这些结果进行融合，并基于最优系数进行加权平均生成最终输出。实验结果表明，在所提出的集成系统中使用布谷鸟搜索优化能显著降低预测误差。将所提系统的评估结果与同类算法进行比较，证明本模型具有更高精度，可为实际场景中的股价指数预测提供有效工具。