Testing for Linear and Nonlinear Gaussian Processes in Nonstationary Time Series

Autor(es)

Rios, Ricardo Araujo ^[1] ; Small, Michael ^[2] ; de Mello, Rodrigo Fernandes ^[3]

Afiliação do(s) autor(es)

^[1] Univ Fed Bahia, Dept Comp Sci, Salvador, BA - Brazil

^[2] Univ Western Australia, Sch Math & Stat, Crawley, WA 6009 - Australia

^[3] Univ Sao Paulo, Inst Math & Comp Sci, Sao Carlos, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS; v. 25, n. 1 JAN 2015.
• Citações Web of Science: 4

Resumo

Surrogate data methods have been widely applied to produce synthetic data, while maintaining the same statistical properties as the original. By using such methods, one can analyze certain properties of time series. In this context, Theiler's surrogate data methods are the most commonly considered approaches. These are based on the Fourier transform, limiting them to be applied only on stationary time series. Consequently, time series including nonstationary behavior, such as trend, produces spurious high frequencies with Theiler's methods, resulting in inconsistent surrogates. To solve this problem, we present two new methods that combine time series decomposition techniques and surrogate data methods. These new methods initially decompose time series into a set of monocomponents and the trend. Afterwards, traditional surrogate methods are applied on those individual monocomponents and a set of surrogates is obtained. Finally, all individual surrogates plus the trend signal are combined in order to create a single surrogate series. Using this method, one can investigate linear and nonlinear Gaussian processes in time series, irrespective of the presence of nonstationary behavior. (AU)

• Processo FAPESP: 11/02655-9 - Análise de influências provenientes da tomada de decisões centralizadas e distribuídas no escalonamento de processos
• Beneficiário: Rodrigo Fernandes de Mello
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 09/18293-9 - Uma Abordagem Híbrida para Identificação e Modelagem de Componentes Estocásticos e Determinísticos presentes em Séries Temporais
• Beneficiário: Ricardo Araújo Rios
• Modalidade de apoio: Bolsas no Brasil - Doutorado