Stock price prediction method of long-term and short-term memory neural network based on attention mechanism

Abstract

The invention discloses a stock price prediction method based on a long-term and short-term memory neural network of an attention mechanism, and belongs to the field of deep learning and stock prediction. The method comprises the following steps of S1, obtaining stock historical data, performing data preprocessing on the stock historical data, and dividing the stock historical data into a trainingset and a test set; s2, performing data standardization on the training set and the test set, and performing wavelet transform processing on data of the training set to remove noise of the financialsequence; s3, initializing parameters required by the long-term and short-term memory neural network prediction model, constructing the long-term and short-term memory neural network prediction model,adding an attention mechanism layer into the long-term and short-term memory neural network prediction model, and training the long-term and short-term memory neural network prediction model by usingtraining set data; and S4, predicting the test set by using the trained prediction model to obtain a prediction result. According to the invention, the nonlinear change of the stock price can be better predicted.

Description

technical field [0001] The invention relates to the fields of deep learning and stock prediction, in particular to a stock price prediction method based on a long-short-term memory neural network of an attention mechanism. Background technique [0002] Due to the characteristics of the stock market, such as high volatility, various market types, and data redundancy, stock forecasting is quite challenging, and stock price forecasting has always been one of the concerns of people; in the past period of time, the traditional Technical analysis methods play a very important role in stock analysis and forecasting, but as the magnitude of stock data increases, traditional technical methods may not be able to meet the changing speed of stock price trends. In addition, the volatility of the stock market is a very important factor. For a linear multivariable dynamic system, it is subjective to predict it only by relying on personal intuition and judgment, and it is very easy to be af...

AI Technical Summary

Problems solved by technology

[0005] In order to solve the problem that the existing stock price prediction model cannot produce ideal prediction results, the present invention provides a stock price prediction method based on the long-term short-term memory neural network of the attention mechanism

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