This research explores how synthetic data generation can overcome limitations in time series analysis, particularly when leveraging foundation models for complex systems.

• Foundation models (TSFMs and TSLLMs) enable task-agnostic learning for time series data
• Data constraints (regulatory, diversity, quality, quantity) limit the effectiveness of these models
• Synthetic data provides a promising solution to address these limitations
• Applications span engineering systems for predictive maintenance, anomaly detection, and complex systems modeling

For engineering professionals, this research provides frameworks to generate high-quality synthetic time series data that can enhance model performance while addressing data scarcity and privacy concerns.

Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Models