Stock trend prediction method, device, computer equipment and storage medium

Abstract

The embodiment of the invention discloses a stock trend prediction method, a device, computer equipment and a storage medium. The method comprises steps of collecting at least two historical stock data sets, wherein each historical stock data set is divided into a training set and a test set, and the at least two historical stock data sets comprise a target historical stock data set of a to-be-predicted stock; establishing an approximately irrelevant regression model according to each training set and each to-be-trained extreme learning machine model corresponding to each historical stock dataset; determining a hidden layer output weight of each to-be-trained extreme learning machine model according to the approximate irrelevant regression model to obtain each trained extreme learning machine model; and inputting a test set obtained by dividing the target historical stock data set into the corresponding trained extreme learning machine model so as to predict the trend of the to-be-predicted stock. By considering the correlation between different data sets, the classification performance of the prediction model is improved, so that the stock prediction accuracy is improved.

Description

technical field [0001] The embodiments of the present invention relate to the technical field of financial analysis, and in particular to a stock trend prediction method, device, computer equipment and storage medium. Background technique [0002] Since the financial securities market started trading, it has been dominated by electronic transactions, so it has accumulated a large amount of transaction data, including stock market data, company financial information and transaction records, etc. Especially in recent years, China's financial securities market has developed rapidly, the scale of transactions has expanded rapidly, and transaction data has grown exponentially. How to make good use of these huge data information is extremely important. One of them is to use these data to predict future stock prices. [0003] Since the stock market is a complex dynamic system with a certain degree of uncertainty, the amount of information and calculations that need to be processed ...

AI Technical Summary

Problems solved by technology

[0003] Since the stock market is a complex dynamic system with a certain degree of uncertainty, the amount of information and calculations that need to be processed during forecasting is relatively large, and the existing stock forecasting methods basically use a data set to train a Models are used to make predictions, which usually do not achieve ideal prediction results

The correlation between various factors in the stock market is intricate, the primary and secondary relationships are uncertain, and the quantitative relationship is difficult to extract. Therefore, it is very difficult to use conventional forecasting methods to make quantitative analysis of the stock market, and the forecast results are often not accurate enough.

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