Provide a solution for stock price forecasting using a combination of differential evolution methods and random forest
Predicting stock prices is a complex task that has fascinated investors and financial analysts for centuries. With the rise of artificial intelligence and machine learning, researchers have developed various models to forecast stock prices, leveraging historical data and market trends. These models aim to identify patterns and correlations between economic indicators, market news, and stock prices to make accurate predictions. Although extensive research has been done in the field of stock price forecasting, but with the increase in the use of new technologies, it is still necessary to carry out these activities. In this paper, a method based on the integration of differential evolution and random forest are presented for stock price prediction. In the proposed method, random forest can effectively predict stock market prices by reviewing data, and the differential evolution method helps to improve the accuracy of stock market forecasts by choosing the best values for random forest parameters. The results of the implementation of this method on the data of AMD show that up to an average of 74% compared to the methods of random forest, neural network and linear regression with a reduction in training error and also up to an average of 55% compared to the method The mentioned ones have reduced the test error. This issue shows the superiority and effectiveness of the proposed method compared to the compared solutions.