Carlos Soares

No synthetic generation method can fully replace original training data, but Grasynda-P strikes a compelling balance between forecasting accuracy and privacy risk.

Geometrical model alignment reveals hidden divergences in predictive accuracy, challenging traditional measures of functional similarity.

Rejecting high-risk predictions can significantly boost forecasting accuracy, especially for challenging time series.