A Framework for Imbalanced Time-Series Forecasting

Luis P. Silvestrin; Leonardos Pantiskas; Mark Hoogendoorn

doi:10.1007/978-3-030-95467-3_19

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 proceeding › Conference contribution › Academic › peer-review

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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 language	English
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
Pages	250-264
Number of pages	15
Volume	1
ISBN (Electronic)	9783030954673
ISBN (Print)	9783030954666
DOIs	https://doi.org/10.1007/978-3-030-95467-3_19
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

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13163 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	7th International Conference on Machine Learning, Optimization, and Data Science, LOD 2021
City	Virtual, Online
Period	4/10/21 → 8/10/21

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/978-3-030-95467-3_19

A Framework for Imbalanced Time-Series ForecastingFinal published version, 1.04 MBLicence: Article 25fa Dutch Copyright Act

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Silvestrin, L. P., Pantiskas, L., & Hoogendoorn, M. (2022). A Framework for Imbalanced Time-Series Forecasting. In G. Nicosia, V. Ojha, E. La Malfa, G. La Malfa, G. Jansen, P. M. Pardalos, G. Giuffrida, & R. Umeton (Eds.), Machine Learning, Optimization, and Data Science: 7th International Conference, LOD 2021, Grasmere, UK, October 4–8, 2021, Revised Selected Papers, Part I (Vol. 1, pp. 250-264). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13163 LNCS). Springer Science and Business Media Deutschland GmbH. Advance online publication. https://doi.org/10.1007/978-3-030-95467-3_19

Silvestrin, Luis P. ; Pantiskas, Leonardos ; Hoogendoorn, Mark. / A Framework for Imbalanced Time-Series Forecasting. Machine Learning, Optimization, and Data Science: 7th International Conference, LOD 2021, Grasmere, UK, October 4–8, 2021, Revised Selected Papers, Part I. editor / Giuseppe Nicosia ; Varun Ojha ; Emanuele La Malfa ; Gabriele La Malfa ; Giorgio Jansen ; Panos M. Pardalos ; Giovanni Giuffrida ; Renato Umeton. Vol. 1 Springer Science and Business Media Deutschland GmbH, 2022. pp. 250-264 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{f697cf73db3345eea0e42c11dbefdccd,

title = "A Framework for Imbalanced Time-Series Forecasting",

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.",

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Silvestrin, LP , Pantiskas, L & Hoogendoorn, M 2022, A Framework for Imbalanced Time-Series Forecasting. in G Nicosia, V Ojha, E La Malfa, G La Malfa, G Jansen, PM Pardalos, G Giuffrida & R Umeton (eds), Machine Learning, Optimization, and Data Science: 7th International Conference, LOD 2021, Grasmere, UK, October 4–8, 2021, Revised Selected Papers, Part I. vol. 1, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13163 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 250-264, 7th International Conference on Machine Learning, Optimization, and Data Science, LOD 2021, Virtual, Online, 4/10/21. https://doi.org/10.1007/978-3-030-95467-3_19

A Framework for Imbalanced Time-Series Forecasting. / Silvestrin, Luis P.; Pantiskas, Leonardos ; Hoogendoorn, Mark.
Machine Learning, Optimization, and Data Science: 7th International Conference, LOD 2021, Grasmere, UK, October 4–8, 2021, Revised Selected Papers, Part I. ed. / Giuseppe Nicosia; Varun Ojha; Emanuele La Malfa; Gabriele La Malfa; Giorgio Jansen; Panos M. Pardalos; Giovanni Giuffrida; Renato Umeton. Vol. 1 Springer Science and Business Media Deutschland GmbH, 2022. p. 250-264 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13163 LNCS).

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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AU - Silvestrin, Luis P.

AU - Pantiskas, Leonardos

AU - Hoogendoorn, Mark

N1 - 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.

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

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KW - Imbalanced regression

KW - Multivariate time-series

KW - Time-series forecasting

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DO - 10.1007/978-3-030-95467-3_19

M3 - Conference contribution

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SN - 9783030954666

VL - 1

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Machine Learning, Optimization, and Data Science

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A2 - Ojha, Varun

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Silvestrin LP , Pantiskas L , Hoogendoorn M. A Framework for Imbalanced Time-Series Forecasting. In Nicosia G, Ojha V, La Malfa E, La Malfa G, Jansen G, Pardalos PM, Giuffrida G, Umeton R, editors, Machine Learning, Optimization, and Data Science: 7th International Conference, LOD 2021, Grasmere, UK, October 4–8, 2021, Revised Selected Papers, Part I. Vol. 1. Springer Science and Business Media Deutschland GmbH. 2022. p. 250-264. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Epub 2022 Feb 2. doi: 10.1007/978-3-030-95467-3_19

A Framework for Imbalanced Time-Series Forecasting

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

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