  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.
