Stock price prediction method based on ensemble learning model

Abstract

The invention discloses a stock price prediction method based on an ensemble learning model, and belongs to the field of computer data processing, and the method comprises the steps: collecting the historical data of stock prices and the corresponding basic factors affecting the stock prices; grouping the stock price historical data and the basic factors according to the appearing time points andthe fixed time intervals of the basic factors; constructing a stock price prediction model; training a stock price prediction model; testing a stock price prediction model; collecting data of a current stock price and stock basic factor data in real time; and according to the current stock price and the basic factors, inputting the stock price into a stock price prediction model to predict the stock price at a future time point or time period. The characteristics of different subnets, different levels and different weights are adopted, a brand-new prediction model is constructed, the predictedstock price is very accurate after the model is trained and tested, and prediction errors can be greatly reduced.

Description

technical field [0001] The invention relates to the field of computer data processing, in particular to a stock price prediction method based on an integrated learning model. Background technique [0002] Stock prices fluctuate in real time, with great instability and randomness. In the process of stock trading, stock selection and purchase behaviors are often based on people's subjective decisions or when stock prices fall. The stock selection behavior is not based on the prediction of the subsequent price trend of the stock, so there may be greater investment risks. [0003] In order to construct and adopt an appropriate investment strategy to achieve a relatively stable and rational investment method, the application of machine learning technology in the field of securities investment, especially in the application of stock selection and the determination of market entry timing, has been received by researchers. Widely concerned, it is based on the prediction of stock pr...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a stock price prediction method based on an integrated learning model, which solves the technical problems that the accuracy of the prediction results of the existing stock price prediction method is low and the model is not reliable

Images