2 papers
cs.LG2026
Benchmarking Time Series Generation Methods for Privacy-Preserving Forecasting
Luis Amorim, Vitor Cerqueira, Moises Santos +2
Time series forecasting in privacy-sensitive domains often requires training models on released data rather than original observations. Synthetic time series generation has been de…
cs.LG2026
Grasynda: Graph-based Synthetic Time Series Generation
Luis Amorim, Moises Santos, Paulo J. Azevedo +2
Data augmentation is a crucial tool in time series forecasting, especially for deep learning architectures that require a large training sample size to generalize effectively. Howe…