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.
|Title of host publication||Machine Learning, Optimization, and Data Science|
|Subtitle of host publication||7th International Conference, LOD 2021, Grasmere, UK, October 4–8, 2021, Revised Selected Papers, Part I|
|Editors||Giuseppe Nicosia, Varun Ojha, Emanuele La Malfa, Gabriele La Malfa, Giorgio Jansen, Panos M. Pardalos, Giovanni Giuffrida, Renato Umeton|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||15|
|Publication status||Published - 2022|
|Event||7th International Conference on Machine Learning, Optimization, and Data Science, LOD 2021 - Virtual, Online|
Duration: 4 Oct 2021 → 8 Oct 2021
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||7th International Conference on Machine Learning, Optimization, and Data Science, LOD 2021|
|Period||4/10/21 → 8/10/21|
Bibliographical noteFunding Information:
This work has been conducted as part of the Just in Time Maintenance project funded by the European Fund for Regional Development. We also thank Tata Steel Europe for providing the data and technical expertise required for our experiments.
© 2022, Springer Nature Switzerland AG.
- Deep learning
- Imbalanced regression
- Multivariate time-series
- Time-series forecasting