Busca avançada
Ano de início
Entree
(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Time series trend detection and forecasting using complex network topology analysis

Texto completo
Autor(es):
Anghinoni, Leandro [1] ; Zhao, Liang [1] ; Ji, Donghong [2] ; Pan, Heng [3]
Número total de Autores: 4
Afiliação do(s) autor(es):
[1] Univ Sao Paulo, Fac Philosophy Sci & Letters Ribeirao Preto FFCLR, Ribeirao Preto, SP - Brazil
[2] Wuhan Univ, Sch Cyber Sci & Engn, Wuhan, Hubei - Peoples R China
[3] Zhongyuan Univ Technol, Henan Key Lab Publ Opin Intelligent Anal, Zhengzhou, Henan - Peoples R China
Número total de Afiliações: 3
Tipo de documento: Artigo Científico
Fonte: NEURAL NETWORKS; v. 117, p. 295-306, SEP 2019.
Citações Web of Science: 0
Resumo

Extracting knowledge from time series provides important tools for many real applications. However, many challenging problems still open due to the stochastic nature of large amount of time series. Considering this scenario, new data mining and machine learning techniques have continuously developed. In this paper, we study time series based on its topological features, observed on a complex network generated from the time series data. Specifically, we present a trend detection algorithm for stochastic time series based on community detection and network metrics. The proposed model presents some advantages over traditional time series analysis, such as adaptive number of classes with measurable strength and better noise absorption. The appealing feature of this work is to pave a new way to represent time series trends by communities of complex networks in topological space instead of physical space (spatial-temporal space or frequency spectral) as traditional techniques do. Experimental results on artificial and real data-sets shows that the proposed method is able to classify the time series into local and global patterns. As a consequence, it improves the predictability on time series. (C) 2019 Elsevier Ltd. All rights reserved. (AU)

Processo FAPESP: 16/23698-1 - Processos dinâmicos em aprendizado de máquina baseados em redes complexas
Beneficiário:Didier Augusto Vega Oliveros
Linha de fomento: Bolsas no Brasil - Pós-Doutorado
Processo FAPESP: 15/50122-0 - Fenômenos dinâmicos em redes complexas: fundamentos e aplicações
Beneficiário:Elbert Einstein Nehrer Macau
Linha de fomento: Auxílio à Pesquisa - Temático