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

A technology of long-term short-term memory and neural network, which is applied in the field of stock price prediction of long-term short-term memory neural network, can solve the problem that the stock price prediction model cannot produce ideal prediction results, and achieve good prediction and improve the effect of accuracy

Pending Publication Date: 2020-06-02
DALIAN UNIV
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Problems solved by technology

[0005] In order to solve the problem that the existing stock price prediction model cannot produce ideal prediction results, the pre...

Method used

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

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Embodiment 1

[0044] This example uses the Standard & Poor's 500 Index (S&P500), the Dow Jones Industrial Average (DJIA) and the Hang Seng Index (HSI) as historical data sets, where the data of S&P 500 and DJIA are from January 3, 2000 to 2019 On July 1, 2002, the data of HSI is from January 2, 2002 to July 1, 2019. There are 6 basic variables in each data set, including opening price, closing price, highest price, lowest price, adjusted closing price , volume, and divide the historical data set into a training set and a test set;

[0045] Standardize the training set and test set, and use wavelet transform to further process the standardized data. The wavelet basis function uses coif 3, and the number of decomposition layers, threshold and threshold function are determined by parameter adjustment;

[0046] Initialize the parameters and build a long short-term memory neural network prediction model with 9 hidden neurons (as attached figure 2 shown), and use the training set whose step siz...

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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...

Claims

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Application Information

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IPC IPC(8): G06Q40/04G06Q30/02G06N3/04G06N3/08
CPCG06Q40/04G06Q30/0202G06N3/049G06N3/08G06N3/045
Inventor 王宾邱佳玉周士华张强魏小鹏
Owner DALIAN UNIV
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