Few-Shot Learnable Augmentation for Financial Time Series Prediction Under Distribution Shifts

Abstract

We address the problem of distribution shift in financial time series prediction, where the behavior of the time series changes over time. Satisfactory performance of forecasting algorithms requires constant model recalibration or fine-tuning to adapt to the new data distribution. Specifically, the ability to quickly fine-tune a model with only a few training samples available from the new distribution is crucial for many business applications. In this paper, we develop a novel method for learnable data augmentation that effectively adjusts to the new time series distribution with only a few samples. We demonstrate the effectiveness of our method compared to the state-of-the-art augmentation methods on both univariate time series (e.g., stock data) and multivariate time series (e.g., yield rate curves) in the presence of distribution shift due to the COVID market shock in 2020.