Time Series Stock Price Forecasting Based on Genetic Algorithm (GA)-Long Short-Term Memory Network (LSTM) Optimization

TL;DR

This paper presents a novel stock price forecasting method combining Genetic Algorithm and LSTM, achieving high accuracy and stability in predictions by optimizing the model for big data applications.

Contribution

The paper introduces a new hybrid GA-LSTM model for stock prediction, demonstrating improved accuracy and stability over traditional methods in big data contexts.

Findings

MAE reduced from 0.11 to 0.01, indicating improved accuracy

Model predictions closely match actual stock trends

High generalization ability demonstrated with R2 of 0.87

Abstract

In this paper, a time series algorithm based on Genetic Algorithm (GA) and Long Short-Term Memory Network (LSTM) optimization is used to forecast stock prices effectively, taking into account the trend of the big data era. The data are first analyzed by descriptive statistics, and then the model is built and trained and tested on the dataset. After optimization and adjustment, the mean absolute error (MAE) of the model gradually decreases from 0.11 to 0.01 and tends to be stable, indicating that the model prediction effect is gradually close to the real value. The results on the test set show that the time series algorithm optimized based on Genetic Algorithm (GA)-Long Short-Term Memory Network (LSTM) is able to accurately predict the stock prices, and is highly consistent with the actual price trends and values, with strong generalization ability. The MAE on the test set is 2.41, the MSE is 9.84, the RMSE is 3.13, and the R2 is 0.87. This research result not only provides a novel stock price prediction method, but also provides a useful reference for financial market analysis using computer technology and big data.