A Framework for Imbalanced Time-Series Forecasting

Autor: Silvestrin, Luis P., Pantiskas, Leonardos, Hoogendoorn, Mark, Nicosia, Giuseppe, Ojha, Varun, La Malfa, Emanuele, La Malfa, Gabriele, Jansen, Giorgio, Pardalos, Panos M., Giuffrida, Giovanni, Umeton, Renato
Přispěvatelé: Artificial Intelligence (section level), Network Institute, Artificial intelligence, Computer Systems, Computational Intelligence, Nicosia, Giuseppe, Ojha, Varun, La Malfa, Emanuele, La Malfa, Gabriele, Jansen, Giorgio, Pardalos, Panos M., Giuffrida, Giovanni, Umeton, Renato
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Machine Learning, Optimization, and Data Science ISBN: 9783030954666
Machine Learning, Optimization, and Data Science: 7th International Conference, LOD 2021, Grasmere, UK, October 4–8, 2021, Revised Selected Papers, Part I, 1, 250-264
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, 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
Popis: 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.
Databáze: OpenAIRE
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