Stock price prediction method based on ensemble learning model

A technology of integrated learning and forecasting methods, applied in the direction of integrated learning, forecasting, instruments, etc., can solve the problems of unreliable models and low accuracy of forecast results, and achieve the effect of reliable results, high accuracy, and precise price

Pending Publication Date: 2020-03-06
深圳市豪斯莱科技有限公司
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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

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  • Stock price prediction method based on ensemble learning model
  • Stock price prediction method based on ensemble learning model

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Embodiment Construction

[0027] In order to make the object, technical solution and advantages of the present invention clearer, preferred embodiments are given to further describe the present invention in detail. However, it should be noted that many of the details listed in the specification are only for readers to have a thorough understanding of one or more aspects of the present invention, and these aspects of the present invention can be implemented even without these specific details.

[0028] like figure 1 Shown, a kind of stock price prediction method based on integrated learning model of the present invention, described method comprises the following steps:

[0029] Step 1: Collect historical data of stock prices and the corresponding basic factors affecting stock prices. The basic factors include basic factors and technical factors; the basic factors include economic factors, policy factors, industry factors and performance factors, and the technical factors include company technology brea...

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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...

Claims

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Application Information

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IPC IPC(8): G06Q40/04G06N20/20G06Q10/04
CPCG06N20/20G06Q10/04G06Q40/04
Inventor 林希温志刚
Owner 深圳市豪斯莱科技有限公司
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