Improving time series modeling by decomposing and analyzing stochastic and deterministic influences

Autor(es)

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

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Inst Math & Comp Sci, Sao Carlos, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: Signal Processing; v. 93, n. 11, p. 3001-3013, NOV 2013.
• Citações Web of Science: 12

Resumo

This paper proposes a new approach to improve time series modeling by considering stochastic and deterministic influences. Assuming such influences are present in observations, a first decomposition step is required to split them into two components: one stochastic and another deterministic. As second step, models are adjusted on each component and combined to form a hybrid model improving time series analysis. The proposed approach considers the Empirical Mode Decomposition method and a Recurrence Plot-based measurement to decompose and assess stochastic and deterministic influences. Experiments confirmed improvements in time series modeling. (C) 2013 Elsevier B.V. All rights reserved. (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