Evaluating Synthetic Data Quality: Advanced Techniques
Chapter 1: Foundations of Synthetic Data Evaluation
Defining Data Quality Dimensions
Challenges in Evaluating Generated Data
The Fidelity-Utility-Privacy Trade-off
Taxonomy of Evaluation Metrics
Setting Up an Evaluation Environment
Chapter 2: Advanced Statistical Fidelity Assessment
Multivariate Distribution Comparisons
Hypothesis Testing for Distributional Similarity
Correlation and Covariance Structure Analysis
Information-Theoretic Measures
Propensity Score Evaluation
Hands-on practical: Implementing Multivariate Tests
Chapter 3: Evaluating Machine Learning Utility
Train-Synthetic-Test-Real (TSTR) Methodology
Train-Real-Test-Synthetic (TRTS) Methodology
Comparing Downstream Model Performance Metrics
Assessing Feature Importance Consistency
Hyperparameter Optimization Effects
Hands-on practical: Running TSTR Evaluations
Chapter 4: Privacy Assessment Techniques
Understanding Privacy Risks in Synthetic Data
Membership Inference Attacks (MIAs)
Attribute Inference Attacks
Distance-Based Privacy Metrics
Differential Privacy Considerations (if applicable)
Hands-on practical: Implementing a Basic MIA
Chapter 5: Specialized and Model-Specific Metrics
Evaluating Synthetic Images: FID, IS, Precision, Recall
Evaluating Synthetic Text: Perplexity, BLEU Scores
Evaluating Synthetic Time-Series Data
Metrics for GAN Evaluation
Metrics for VAE Evaluation
Hands-on practical: Calculating FID for Image Data
Chapter 6: Building Comprehensive Evaluation Reports
Selecting Appropriate Metrics for the Task
Automating Evaluation Pipelines
Visualizing Evaluation Results Effectively
Interpreting and Communicating Findings
Benchmarking Different Synthetic Datasets
Practice: Generating a Quality Report Snippet
Evaluating Synthetic Time-Series Data
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- Time Series Analysis: Forecasting and Control, George E. P. Box, Gwilym M. Jenkins, Gregory C. Reinsel, Greta M. Ljung, 2015 (John Wiley & Sons) - Provides foundational theory for time series analysis, including autocorrelation, partial autocorrelation, cross-correlation, and models like ARIMA, which are essential for understanding temporal dependencies.
- Forecasting: Principles and Practice, Rob J Hyndman, George Athanasopoulos, 2021 (OTexts) - An excellent, freely available resource covering practical aspects of time series forecasting, including seasonal-trend decomposition (STL), various forecasting methods, and utility evaluation.
- Time-series Generative Adversarial Networks (TimeGAN): A Way to Simulate Diverse Time-series Datasets, Jinsung Yoon, Daniel Jarrett, Mihaela van der Schaar, 2019 Advances in Neural Information Processing Systems, Vol. 32 (NeurIPS) DOI: 10.5555/3454287.3455243 - Introduces a method for generating synthetic time series and provides a framework for evaluating the quality and utility of synthetic time series data, including statistical comparisons and forecasting utility.
- Introduction to Time Series and Forecasting, Peter J. Brockwell, Richard A. Davis, 2016 (Springer International Publishing) DOI: 10.1007/978-3-319-29854-2 - A comprehensive textbook on time series analysis, covering spectral analysis, which is essential for understanding and comparing Power Spectral Density (PSD) characteristics.