LSTM-Attention Stock Predictor

A deep learning model for stock price prediction using Long Short-Term Memory (LSTM) networks with an attention mechanism. It captures long-range dependencies in time-series data while prioritizing key historical patterns for improved predictive accuracy, making it adaptable to various stocks and market conditions.

Table of Contents

• Technical Overview
• Frameworks and Libraries
• Approach
• Performance Metrics
• Accuracy Calculation
• Future Enhancements

Technical Overview

This project employs advanced deep learning techniques to analyze historical stock price data and predict future price movements.

Frameworks and Libraries

• TensorFlow: For building and training the deep learning model.
• Keras: For simplifying model development with high-level APIs.
• Python: The primary programming language used for implementation.
• yfinance: For fetching real-time stock data.
• matplotlib: For data visualization.
• mplfinance: For advanced financial visualizations.

Approach

1. Data Collection: Real-time stock data is fetched using the yfinance library.
2. Data Preprocessing: The stock price data is cleaned and normalized using MinMaxScaler.
3. Model Architecture:
   • Built a multi-layer LSTM model enhanced with an attention mechanism to capture long-term dependencies in the data.
4. Evaluation: Utilized metrics like Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) to assess model performance.

Performance Metrics for 'TSLA' stock

• Test Loss: 0.000544
• Mean Absolute Error (MAE): 0.0187
• Root Mean Square Error (RMSE): 0.0233

These metrics indicate that the model performs well in predicting stock price movements with minimal error.