Applying Empirical Mode Decomposition and mutual information to separate stochastic and deterministic influences embedded in signals

Autor(es)

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

Afiliação do(s) autor(es)

^[1] Univ Fed Bahia, Dept Comp Sci, BR-41170290 Salvador, BA - Brazil

^[2] Univ Sao Paulo, Inst Math & Comp Sci, BR-05508 Sao Paulo - Brazil

• Tipo de documento: Artigo Científico
• Fonte: Signal Processing; v. 118, p. 159-176, JAN 2016.
• Citações Web of Science: 12

Resumo

Empirical Mode Decomposition (EMD) is a method to decompose signals into Intrinsic Mode Functions (IMB) to be analyzed in terms of instantaneous frequencies and amplitudes. By comparing the phase spectra of IMEs, we observed that a subset of them contains more stochastic influences while the other is predominantly deterministic. Considering this observation, we claim that IMEs can be combined to form two additive components: one deterministic and another stochastic. Having both components separated, researchers can improve data modeling as well as forecasting. In this context, this paper presents a new approach to separate deterministic from stochastic influences embedded in signals, considering the mutual information contained in phase spectra of consecutive IMFs. As previous step of this study, we also proved that EMD works as a filter bank. (C) 2015 Elsevier B.V. All rights reserved. (AU)

• Processo FAPESP: 14/13323-5 - Abordagem baseada na estabilidade de algoritmos de agrupamento de dados para garantir a detecção de mudanças de conceito em fluxos de dados
• 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