A Framework for Imbalanced Time-series Forecasting

TL;DR

This paper introduces a general framework for addressing imbalanced moments in time-series forecasting, emphasizing practical solutions validated through a case study in an industrial setting.

Contribution

It proposes a novel approach specifically designed to handle imbalanced moments in time-series data, with practical applicability demonstrated via a real-world case study.

Findings

Effective in identifying underrepresented moments in time-series data

Improves forecasting accuracy for rare events

Validated through a large industrial case study

Abstract

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.