Stock price prediction method of gated cycle unit neural network based on Kalman filtering

Abstract

The invention discloses a stock price prediction method of a gated recurrent unit neural network based on Kalman filtering, and belongs to the field of deep learning and stock prediction, and the method comprises the steps: S1, obtaining stock historical data, dividing the data into a training set and a test set, and carrying out the preprocessing; S2, initializing parameters required by prediction of the gating cycle unit neural network, constructing a gating cycle unit neural network model, and training and storing the gating cycle unit neural network model; S3, using the trained predictionmodel to predict the test set data, and using a Kalman filtering algorithm to perform denoising optimization on the obtained prediction result sequence; and S4, evaluating the performance of the prediction model for the denoised and optimized result in S3 by using the root-mean-square error and the determination coefficient. The stock price is predicted through the gating cycle unit neural network, and the prediction result sequence and the true value are compared and optimized by using the Kalman filtering algorithm so as to achieve the more accurate prediction effect.

Description

Technical field [0001] The present invention belongs to the field of deep learning and stock prediction. More specifically, it relates to a method for predicting stock prices based on Kalman filter-based gated recurrent unit neural network, which uses historical stock price sequences to accurately predict future stock prices. Background technique [0002] The stock market is quite challenging due to its high volatility, various types of markets, and data redundancy. The stock price prediction has always been one of the issues that people care about. 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 speed of changes in stock price trends. In addition, the volatility of the stock market is a non-linear multivariable dynamic system. It is quite subjective to predict it only relying on personal intui...

AI Technical Summary

Problems solved by technology

[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 issues that people care about. 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 nonlinear multi-variable dynamic system. It is quite subjective to predict it only by relying on personal intuition and judgment.

[0003] The financial time series has the characteristics of non-stationary, nonlinear, and high noise. The stock price series we see also contains noise, which will make the useful series overlap with the noise, so that the traditional forecasting method cannot produce ideal forecasting results.

Images