  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.
