Predicting Stock Market Trends Using Deep Long Short-Term Memory Networks

Predicting stock market trends is a highly challenging task due to the complex and volatile nature of financial markets. This project aims to utilize Long Short-Term Memory (LSTM) networks to predict stock market trends based on historical data. LSTMs, a type of recurrent neural network (RNN), are particularly suited for time series forecasting due to their ability to learn from sequential data. By collecting and preprocessing a dataset of historical stock prices and market indicators, we develop and train an LSTM model to predict future stock prices.

The model's performance is evaluated using metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), demonstrating its accuracy and effectiveness in predicting stock market trends. The insights derived from the model can be valuable for investors and traders in making informed decisions.

Keywords: Stock Market Prediction, LSTM Networks, Time Series Forecasting, Deep Learning, Financial Market, Feature Engineering, Model Evaluation