Exponential LSTM

This machine learning project demonstrates the implementation of LSTM neural networks for predicting time series data with exponential patterns. It showcases deep learning fundamentals and their practical applications in forecasting and pattern recognition.

Project Overview

The project implements advanced recurrent neural networks to understand and predict complex temporal patterns. LSTM networks are particularly effective at learning long-term dependencies in sequential data, making them ideal for time series problems with exponential growth patterns.

Key Features

• LSTM model architecture design and implementation
• Time series data preprocessing and normalization
• Model training with batch optimization
• Evaluation metrics including MAE, RMSE, and R²
• Visualization of predictions vs actual data
• Hyperparameter tuning for optimal performance
• Comparison between TensorFlow and PyTorch implementations

Model Components

• Input Layer: Normalized time series data
• LSTM Layers: Multiple stacked LSTM cells for feature extraction
• Dense Layers: Fully connected output layers
• Activation: ReLU and sigmoid functions
• Optimization: Adam optimizer with learning rate scheduling
• Regularization: Dropout and batch normalization

Technologies Used

• Language: Python
• ML Frameworks: TensorFlow and Keras, PyTorch
• Data Processing: NumPy for numerical computing, Pandas for data manipulation
• Visualization: Matplotlib and Seaborn for plotting results
• Development Environment: Jupyter Notebooks
• Version Control: Git for project management