The evaluation of synthetic data generation is crucial, especially in the retail sector where data accuracy is paramount. This paper introduces a comprehensive framework for assessing synthetic retail data, focusing on fidelity, utility, and privacy. Our approach differentiates between continuous and discrete data attributes, providing precise evaluation criteria. Fidelity is measured through stability and generalizability. Stability ensures synthetic data accurately replicates known data distributions, while generalizability confirms its robustness in novel scenarios. Utility is demonstrated through the synthetic data's effectiveness in critical retail tasks such as demand forecasting and dynamic pricing, proving its value in predictive analytics and strategic planning. Privacy is safeguarded using Differential Privacy, ensuring synthetic data maintains a perfect balance between resembling training and holdout datasets without compromising security. Our findings validate that this framework provides reliable and scalable evaluation for synthetic retail data. It ensures high fidelity, utility, and privacy, making it an essential tool for advancing retail data science. This framework meets the evolving needs of the retail industry with precision and confidence, paving the way for future advancements in synthetic data methodologies.

翻译：合成数据生成的评估至关重要，尤其在数据准确性至上的零售领域。本文提出了一个评估零售合成数据的综合框架，重点关注保真度、效用性和隐私性。我们的方法区分连续与离散数据属性，提供精确的评估标准。保真度通过稳定性和泛化性衡量：稳定性确保合成数据准确复现已知数据分布，而泛化性则确认其在未知场景中的鲁棒性。效用性通过合成数据在需求预测和动态定价等关键零售任务中的有效性得以证明，验证了其在预测分析和战略规划中的价值。隐私性通过差分隐私技术予以保障，确保合成数据在保持与训练集及保留集相似性的同时，不损害安全性。研究结果表明，该框架为零售合成数据提供了可靠且可扩展的评估方案，确保了高保真度、高效用性和强隐私性，成为推进零售数据科学的重要工具。本框架精准且可靠地满足了零售行业不断发展的需求，为合成数据方法的未来进步铺平了道路。