Online event detection in streaming time series: novel metrics and practical insights (2024)
- Authors:
- Autor USP: FERREIRA, JOÃO EDUARDO - IME
- Unidade: IME
- DOI: 10.1109/IJCNN60899.2024.10650809
- Subjects: STREAMING; ANÁLISE DE SÉRIES TEMPORAIS
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher: IEEE
- Publisher place: Piscataway
- Date published: 2024
- Source:
- Título: Proceedings
- Conference titles: International Joint Conference on Neural Networks - IJCNN
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
LIMA, Janio et al. Online event detection in streaming time series: novel metrics and practical insights. 2024, Anais.. Piscataway: IEEE, 2024. Disponível em: https://doi.org/10.1109/IJCNN60899.2024.10650809. Acesso em: 21 jan. 2026. -
APA
Lima, J., Tavares, L. G., Pacitti, E., Ferreira, J. E., Santos, I., Siqueira, I. G., et al. (2024). Online event detection in streaming time series: novel metrics and practical insights. In Proceedings. Piscataway: IEEE. doi:10.1109/IJCNN60899.2024.10650809 -
NLM
Lima J, Tavares LG, Pacitti E, Ferreira JE, Santos I, Siqueira IG, Porto F, Carvalho D, Coutinho R, Ogasawara E. Online event detection in streaming time series: novel metrics and practical insights [Internet]. Proceedings. 2024 ;[citado 2026 jan. 21 ] Available from: https://doi.org/10.1109/IJCNN60899.2024.10650809 -
Vancouver
Lima J, Tavares LG, Pacitti E, Ferreira JE, Santos I, Siqueira IG, Porto F, Carvalho D, Coutinho R, Ogasawara E. Online event detection in streaming time series: novel metrics and practical insights [Internet]. Proceedings. 2024 ;[citado 2026 jan. 21 ] Available from: https://doi.org/10.1109/IJCNN60899.2024.10650809 - P-SaMI: a data-flow pattern to perform massively-parallel molecular docking experiments using a fully-flexible receptor model
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Informações sobre o DOI: 10.1109/IJCNN60899.2024.10650809 (Fonte: oaDOI API)
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