To explore the use of Long Short Term Memory (LSTM) networks in predicting stock prices and trends.
To investigate the effectiveness of LSTM networks in comparison to traditional machine learning algorithms for stock forecasting.
To develop a robust and accurate model for predicting stock prices using LSTM networks.
Conduct a literature review on the application of LSTM networks for financial predictions.
Collect and preprocess a dataset of historical stock prices for training and testing the LSTM model.
Implement and train an LSTM network for stock price prediction.
Evaluate the performance of the LSTM model using metrics such as mean squared error and accuracy.
Compare the results of the LSTM model with other traditional machine learning algorithms for stock forecasting.
Write a comprehensive report documenting the methodology, results, and implications of using LSTM networks for intelligent financial predictions.
