Don't overfit the history -- Recursive time series data augmentation

Understand

Time series observations can be seen as realizations of an underlying dynamical system governed by rules that we typically do not know.

• For time series learning tasks, we need to understand that we fit our model on available data, which is a unique realized history.
• Training on a single realization often induces severe overfitting lacking generalization.
• To address this issue, we introduce a general recursive framework for time series augmentation, which we call Recursive Interpolation Method, denoted as RIM.