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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.)

Learning and distinguishing time series dynamics via ordinal patterns transition graphs

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Author(s):
Borges, Joao B. [1, 2] ; Ramos, Heitor S. [1] ; Mini, Raquel A. F. [3] ; Rosso, Osvaldo A. [4, 5, 6] ; Frery, Alejandro C. [7] ; Loureiro, Antonio A. F. [1]
Total Authors: 6
Affiliation:
[1] Univ Fed Minas Gerais, Dept Comp Sci, BR-30123970 Belo Horizonte, MG - Brazil
[2] Univ Rio Grande do Norte, Dept Comp & Technol, BR-59300000 Caico, RN - Brazil
[3] Pontificia Univ Catolica Minas Gerais, Dept Comp Sci, BR-30535610 Belo Horizonte, MG - Brazil
[4] Consejo Nacl Invest Cient & Tecn, Buenos Aires, DF - Argentina
[5] Univ Fed Alagoas, Inst Phys, BR-57072900 Maceio, AL - Brazil
[6] Univ Inst Italian Hosp, Hosp Italiano Buenos Aires, Inst Translat Med & Biomed Engn, Buenos Aires, DF - Argentina
[7] Univ Fed Alagoas, Lab Sci Computat & Numer Anal, BR-57072970 Maceio, AL - Brazil
Total Affiliations: 7
Document type: Journal article
Source: Applied Mathematics and Computation; v. 362, DEC 1 2019.
Web of Science Citations: 0
Abstract

Strategies based on the extraction of measures from ordinal patterns transformation, such as probability distributions and transition graphs, have reached relevant advancements in distinguishing different time series dynamics. However, the reliability of such measures depends on the appropriate selection of parameters and the need for large time series. In this paper we present a method for the characterization of distinct time series behaviors based on the probability of self-transitions, a measure extracted from their transformation onto ordinal patterns transition graphs. We validate our method by investigating the main characteristics of periodic, random, and chaotic time series. By the application of learning strategies, we precisely classify different randomness levels in time series, reaching 100% in accuracy, and advances in performing the hard task of distinguishing random noises from chaotic time series, correctly distinguishing 96.61% of the cases. Furthermore, we show that this strategy is well suitable to be used by many applications, even for short time series, and does not depend on the selection of parameters. (C) 2019 Elsevier Inc. All rights reserved. (AU)

FAPESP's process: 15/24536-2 - Design of services and applications for IOT
Grantee:Antonio Alfredo Ferreira Loureiro
Support Opportunities: Regular Research Grants