Stock Price Prediction Using LSTM

Abstract

Stock price prediction is a challenging task that has gained significant attention due to its potential impact on financial decision-making. In this study, we propose a stock price prediction model using Long Short-Term Memory (LSTM), a type of recurrent neural network (RNN) known for its ability to capture temporal dependencies in sequential data.

The proposed LSTM-based model aims to predict the future prices of stocks by analyzing historical market data. The model utilizes a large dataset of historical stock prices as input and learns the patterns and trends inherent in the data to make accurate predictions.

To evaluate the performance of the LSTM model, we conduct experiments on real-world stock market datasets. The results demonstrate that our model outperforms traditional prediction methods and achieves higher accuracy in forecasting stock prices.

Furthermore, we explore the impact of different architectural configurations, such as the number of LSTM units and training epochs, on prediction accuracy. By fine-tuning these parameters, we can optimize the performance of the LSTM model and achieve even better results.

The findings of this study highlight the potential of LSTM-based models in stock price prediction. The ability to accurately forecast stock prices can provide valuable insights for investors and traders, enabling them to make informed decisions in dynamic and unpredictable financial markets