Chapter 5: Data-to-Data Models

Introduction: The Data Scarcity Problem

Part I: Missing Data Imputation with Autoencoders

The Challenge

Implementation

Autoencoder Architecture

Training with Early Stopping

Evaluation Results

Figure 5.3: Neural-Network–Based Imputation Results and Quality Assessment

Diffusion Models for Missing Data Imputation (2024–2025)

Part II: Synthetic Data Generation with GANs

The Mode Collapse Problem

Improved GAN Architecture

Training with Gradient Penalty

Quality Assessment

Synthetic Tabular Data: 2025 Landscape

Part III: Variational Autoencoders (VAEs)

Why VAEs?

Robust VAE Architecture

Training with KL Annealing

VAE Quality Assessment

Part IV: Gaussian Process Spatial Interpolation

The Challenge

Implementation

Gaussian Process Fitting

Uncertainty Analysis

Part V: Time Series Gap Filling

The Challenge

Enhanced Bidirectional LSTM

Training

Evaluation

Summary and Key Takeaways

🎯 Results Summary

📊 Method Comparison

💡 Key Lessons

⚠️ Important Reminders

🚀 Practical Guidelines

📈 Performance Benchmarks

Next Steps

References

Additional Resources