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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Time series trend detection and forecasting using complex network topology analysis

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Author(s):
Anghinoni, Leandro [1] ; Zhao, Liang [1] ; Ji, Donghong [2] ; Pan, Heng [3]
Total Authors: 4
Affiliation:
[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
Total Affiliations: 3
Document type: Journal article
Source: NEURAL NETWORKS; v. 117, p. 295-306, SEP 2019.
Web of Science Citations: 0
Abstract

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)

FAPESP's process: 15/50122-0 - Dynamic phenomena in complex networks: basics and applications
Grantee:Elbert Einstein Nehrer Macau
Support type: Research Projects - Thematic Grants
FAPESP's process: 16/23698-1 - Dynamical processes in complex network based on machine learning
Grantee:Didier Augusto Vega Oliveros
Support type: Scholarships in Brazil - Post-Doctorate