Time series trend detection and forecasting using complex network topology analysis (2018)
- Authors:
- USP affiliated authors: LIANG, ZHAO - FFCLRP ; ANGHINONI, LEANDRO - FFCLRP
- Unidade: FFCLRP
- DOI: 10.1109/ijcnn.2018.8489167
- Assunto: REDES COMPLEXAS
- Keywords: Time series; Trend detection; Complex networks; Community detection
- Language: Inglês
- Imprenta:
- Publisher place: Rio de Janeiro
- Date published: 2018
- Source:
- Título: Annals
- Conference titles: International Joint Conference on Neural Networks (IJCNN)
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
ANGHINONI, Leandro et al. Time series trend detection and forecasting using complex network topology analysis. 2018, Anais.. Rio de Janeiro: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo, 2018. Disponível em: https://doi.org/10.1109/ijcnn.2018.8489167. Acesso em: 28 fev. 2026. -
APA
Anghinoni, L., Liang, Z., Zheng, Q., & Zhang, J. (2018). Time series trend detection and forecasting using complex network topology analysis. In Annals. Rio de Janeiro: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo. doi:10.1109/ijcnn.2018.8489167 -
NLM
Anghinoni L, Liang Z, Zheng Q, Zhang J. Time series trend detection and forecasting using complex network topology analysis [Internet]. Annals. 2018 ;[citado 2026 fev. 28 ] Available from: https://doi.org/10.1109/ijcnn.2018.8489167 -
Vancouver
Anghinoni L, Liang Z, Zheng Q, Zhang J. Time series trend detection and forecasting using complex network topology analysis [Internet]. Annals. 2018 ;[citado 2026 fev. 28 ] Available from: https://doi.org/10.1109/ijcnn.2018.8489167 - TransGNN: a transductive graph neural network with graph dynamic embedding
- Characterizing data patterns with core-periphery network modeling
- Time series pattern identification by hierarchical community detection
- Temporal network pattern identification by community modelling
- Classificação e previsão de séries temporais através de redes complexas
- Structure characterization of complex networks for machine learning
- Analysis of the effectiveness of public health measures on COVID-19 transmission
- Semi-supervised learning with concept drift using particle dynamics applied to network intrusion detection data
- Computer-aided music composition with LSTM neural network and chaotic inspiration
- Semi-supervised learning by edge domination in complex networks
Informações sobre o DOI: 10.1109/ijcnn.2018.8489167 (Fonte: oaDOI API)
Download do texto completo
| Tipo | Nome | Link | |
|---|---|---|---|
 |
002965542.pdf |
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
