Time Series Synthesis

Time series synthesis focuses on generating artificial time series data that accurately reflects the statistical properties of real-world data. Current research emphasizes developing models capable of handling diverse data characteristics, including irregularities, missing values, and high dimensionality, employing architectures such as diffusion models, generative adversarial networks (GANs), and transformers, often combined with autoencoders or variational autoencoders. This field is crucial for addressing data scarcity, enhancing privacy through data anonymization, and facilitating tasks like data augmentation and scenario exploration across various scientific and engineering domains.