Source: Lecture Notes in Artificial Intelligence. Conference titles: Brazilian Conference on Intelligent Systems -BRACIS 2023: Proceedings, Part 1. Unidade: EP
Subjects: REDES NEURAIS, APRENDIZADO COMPUTACIONAL, PREVISÃO (ANÁLISE DE SÉRIES TEMPORAIS)
ABNT
BARROS, Marcel Rodrigues de et al. Embracing data irregularities in multivariate time series with recurrent and graph neural networks. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-031-45368-7_1. Acesso em: 10 nov. 2025. , 2023APA
Barros, M. R. de, Rissi, T. L., Cabrera, E. F., Tannuri, E. A., Gomi, E. S., Barreira, R. A., & Reali Costa, A. H. (2023). Embracing data irregularities in multivariate time series with recurrent and graph neural networks. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-031-45368-7_1NLM
Barros MR de, Rissi TL, Cabrera EF, Tannuri EA, Gomi ES, Barreira RA, Reali Costa AH. Embracing data irregularities in multivariate time series with recurrent and graph neural networks [Internet]. Lecture Notes in Artificial Intelligence. 2023 ; 14195 3-17.[citado 2025 nov. 10 ] Available from: https://doi.org/10.1007/978-3-031-45368-7_1Vancouver
Barros MR de, Rissi TL, Cabrera EF, Tannuri EA, Gomi ES, Barreira RA, Reali Costa AH. Embracing data irregularities in multivariate time series with recurrent and graph neural networks [Internet]. Lecture Notes in Artificial Intelligence. 2023 ; 14195 3-17.[citado 2025 nov. 10 ] Available from: https://doi.org/10.1007/978-3-031-45368-7_1

