A Framework for Imbalanced Time-series Forecasting
It addresses a practical problem in domains like energy and finance where accurate prediction of rare events is crucial, but the approach appears incremental as it builds on existing imbalanced regression concepts.
The paper tackles imbalanced time-series forecasting by focusing on underrepresented moments, developing a general approach to reduce imbalances, and demonstrates it through a case study in a large industrial company.
Time-series forecasting plays an important role in many domains. Boosted by the advances in Deep Learning algorithms, it has for instance been used to predict wind power for eolic energy production, stock market fluctuations, or motor overheating. In some of these tasks, we are interested in predicting accurately some particular moments which often are underrepresented in the dataset, resulting in a problem known as imbalanced regression. In the literature, while recognized as a challenging problem, limited attention has been devoted on how to handle the problem in a practical setting. In this paper, we put forward a general approach to analyze time-series forecasting problems focusing on those underrepresented moments to reduce imbalances. Our approach has been developed based on a case study in a large industrial company, which we use to exemplify the approach.