Data streams are time series: challenging assumptions (2020)
- Authors:
- Autor USP: MELLO, RODRIGO FERNANDES DE - ICMC
- Unidade: ICMC
- DOI: 10.1007/978-3-030-61380-8_36
- Subjects: APRENDIZADO COMPUTACIONAL; ANÁLISE DE SÉRIES TEMPORAIS
- Keywords: Data streams; Time series; Statistical Learning Theory
- Language: Inglês
- Imprenta:
- Source:
- Título: Lecture Notes in Artificial Intelligence
- ISSN: 0302-9743
- Volume/Número/Paginação/Ano: v. 12320, p. 529-543, 2020
- Conference titles: Brazilian Conference on Intelligent Systems - BRACIS
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
READ, Jesse et al. Data streams are time series: challenging assumptions. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-030-61380-8_36. Acesso em: 02 nov. 2024. , 2020 -
APA
Read, J., Rios, R. A., Nogueira, T., & Mello, R. F. de. (2020). Data streams are time series: challenging assumptions. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-030-61380-8_36 -
NLM
Read J, Rios RA, Nogueira T, Mello RF de. Data streams are time series: challenging assumptions [Internet]. Lecture Notes in Artificial Intelligence. 2020 ; 12320 529-543.[citado 2024 nov. 02 ] Available from: https://doi.org/10.1007/978-3-030-61380-8_36 -
Vancouver
Read J, Rios RA, Nogueira T, Mello RF de. Data streams are time series: challenging assumptions [Internet]. Lecture Notes in Artificial Intelligence. 2020 ; 12320 529-543.[citado 2024 nov. 02 ] Available from: https://doi.org/10.1007/978-3-030-61380-8_36 - Caracterização de comportamento de sistemas por meio de agrupamento de dados e detecção de novidades
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Informações sobre o DOI: 10.1007/978-3-030-61380-8_36 (Fonte: oaDOI API)
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