Data augmentation for time series regression: Applying transformations, autoencoders and adversarial networks to electricity price forecasting

A model’s expected generalisation error is inversely proportional to its training set size. This relationship can pose a problem when modelling multivariate time series, because structural breaks, low sampling rates, and high data gathering costs can severely restrict training set sizes, increasing...

Ausführliche Beschreibung