Synthetic Time Series Data Generation using GANs

🌟 Overview

This repository introduces a cutting-edge Generative Adversarial Network (GAN) designed for synthetic time series data generation, with a specialized focus on medical signals like ECG (Electrocardiogram). The project leverages an innovative architecture to generate high-fidelity synthetic data with remarkable precision.

✨ Key Features

• Advanced Synthetic Data Generation

  • StaGAN architecture for time series synthesis
  • Specialized in medical signal replication
  • Privacy-preserving data augmentation

• Unique Neural Network Architecture

  • Generator: LSTM/BiLSTM-based neural network
  • Discriminator: Convolutional Neural Network (CNN) with Minibatch Discrimination (MBD)

🔬 Research Context

The project addresses critical challenges in medical data research:

• Limited access to comprehensive medical datasets
• Privacy constraints in healthcare data sharing
• Need for robust machine learning training data

🚀 Potential Applications

• Medical Signal Analysis
• ECG Pattern Generation
• Machine Learning Model Training
• Signal Processing Research
• Data Anonymization Techniques

📦 Prerequisites

System Requirements

• Python 3.8+
• CUDA-capable GPU (recommended)
• Minimum 16GB RAM

Dependencies

• PyTorch
• NumPy
• Pandas
• Matplotlib
• Seaborn
• scikit-learn

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🧠 Model Architecture

Generator

• Architecture: LSTM/BiLSTM
• Purpose: Generate realistic synthetic time series data
• Key Features:
  • Sequence generation
  • Long-term dependency capture
  • High-dimensional data synthesis

Discriminator

• Architecture: Convolutional Neural Network (CNN)
• Technique: Minibatch Discrimination (MBD)
• Purpose: Distinguish between real and generated data
• Key Features:
  • Feature extraction
  • Detailed pattern recognition
  • Statistical distribution matching

🏋️ Training Pipeline

Data Preparation

• Load time series dataset
• Normalize and preprocess signals
• Create training and validation splits

Training Process

• Adversarial training mechanism
• Alternating generator and discriminator updates
• Performance metric tracking
• Model checkpointing

📊 Evaluation Metrics

• Maximum Mean Discrepancy (MMD)