A Framework for Imbalanced Time-Series Forecasting

Luis P. Silvestrin*, Leonardos Pantiskas, Mark Hoogendoorn

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

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.

Original languageEnglish
Title of host publicationMachine Learning, Optimization, and Data Science - 7th International Conference, LOD 2021, Revised Selected Papers
EditorsGiuseppe Nicosia, Varun Ojha, Emanuele La Malfa, Gabriele La Malfa, Giorgio Jansen, Panos M. Pardalos, Giovanni Giuffrida, Renato Umeton
PublisherSpringer Science and Business Media Deutschland GmbH
Pages250-264
Number of pages15
Volume13163
ISBN (Print)9783030954666
DOIs
Publication statusPublished - 2022
Event7th International Conference on Machine Learning, Optimization, and Data Science, LOD 2021 - Virtual, Online
Duration: 4 Oct 20218 Oct 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13163 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th International Conference on Machine Learning, Optimization, and Data Science, LOD 2021
CityVirtual, Online
Period4/10/218/10/21

Bibliographical note

Funding 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.

Publisher Copyright:
© 2022, Springer Nature Switzerland AG.

Keywords

  • Deep learning
  • Imbalanced regression
  • Multivariate time-series
  • Time-series forecasting

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