Research on Stock Price Prediction Based on LSTM Model and Random Forest

Abstract

In this study, cutting-edge methods of applying deep learning techniques to stock market predictions were explored, specifically focusing on the stock data of Tesla Inc. Long Short-Term Memory networks (LSTMs), an advanced form of Recurrent Neural Networks (RNNs) capable of effectively addressing the issues of vanishing and exploding gradients that traditional RNNs face, were employed. This enhances the model's learning capability and predictive accuracy for time series data. The innovation of this research lies in the integration of the LSTM model with the Random Forest algorithm, forming a hybrid model aimed at leveraging the complementary strengths of both models to improve the accuracy of stock price predictions. Through empirical analysis of Tesla's stock data, it was found that the hybrid model outperformed the individual LSTM model. This result not only proved the effectiveness of LSTMs in handling complex time series prediction problems but also demonstrated the potential of enhancing predictive performance by integrating different types of models. The findings offer a new perspective for financial market analysis and prediction, especially in the use of deep learning technologies for stock price forecasting. They provide valuable references for future research and practice in this field. Further investigations could explore the applicability of this hybrid approach to other financial instruments and markets.