This project focuses on utilizing Long Short-Term Memory (LSTM) recurrent neural networks to predict future stock prices based on historical data from the S&P 500 dataset. LSTM models demonstrate superior performance compared to traditional linear models, offering valuable insights for investors.
The primary objective is to forecast stock prices and trends by leveraging historical data. LSTM networks, known for capturing temporal dependencies, are employed to model the time series data effectively. The project involves data preprocessing, model training, and performance evaluation.
- Python: Programming language for data preprocessing, model development, and evaluation.
- TensorFlow: Deep learning framework utilized for building and training LSTM models.
- Pandas: Library for data manipulation and preprocessing.
- Matplotlib: Plotting library for visualizing data and model performance.
- Scikit-learn: Machine learning library for evaluation metrics and preprocessing techniques.
- Dataset: S&P 500 dataset containing daily stock prices from 2010 to 2021.
- Preprocessing: Selection of 'Close' column for daily closing prices, followed by MinMax scaling to normalize values between 0 and 1.
- LSTM networks designed to capture long-term dependencies in time series data.
- Comprises memory cells, input gates, forget gates, and output gates.
- Input gates regulate the importance of new information, while forget gates manage retention of previous information.
- Output gates control information flow from memory cells to hidden states, facilitating accurate predictions.
- Model Training: LSTM models trained on historical stock price data.
- Evaluation Metrics: Mean Squared Error (MSE) utilized to assess prediction accuracy.
- Hyperparameters: Batch size and number of epochs optimized for improved model performance.
- Performance Analysis: Evaluation of model losses and prediction accuracy for different epochs.
The LSTM-based approach demonstrates promising results in predicting stock prices and trends. By leveraging deep learning techniques, investors can make informed decisions and optimize their investment strategies.