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Nowadays, the deployment of deep learning models on edge devices for addressing real-world classification problems is becoming more prevalent. Moreover, there is a growing popularity in the approach of early classification, a technique that involves
Externí odkaz:
http://arxiv.org/abs/2306.14606
In current research, machine and deep learning solutions for the classification of temporal data are shifting from single-channel datasets (univariate) to problems with multiple channels of information (multivariate). The majority of these works are
Externí odkaz:
http://arxiv.org/abs/2210.07713
Nowadays, with the rising number of sensors in sectors such as healthcare and industry, the problem of multivariate time series classification (MTSC) is getting increasingly relevant and is a prime target for machine and deep learning approaches. The
Externí odkaz:
http://arxiv.org/abs/2204.01379
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
Externí odkaz:
http://arxiv.org/abs/2107.10709
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
Publikováno v:
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
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
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
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::39c9d177f627c3db3f120a6cb5c84fcb
https://research.vu.nl/en/publications/f697cf73-db33-45ee-a0e4-2c11dbefdccd
https://research.vu.nl/en/publications/f697cf73-db33-45ee-a0e4-2c11dbefdccd