Time Series Generation

Time series generation focuses on creating synthetic time series data that accurately reflects the statistical properties and temporal dynamics of real-world data. Current research emphasizes the development and refinement of generative models, including GANs, VAEs, diffusion models, and transformers, often incorporating techniques like vector quantization and self-supervised learning to improve generation quality and controllability. These advancements are crucial for addressing data scarcity, augmenting existing datasets for improved model training, and enabling simulations in various fields, such as finance, healthcare, and energy systems. The resulting synthetic data enhances anomaly detection, forecasting, and other downstream analytical tasks.