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

Author(s)

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

Affiliation

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

• Document type: Journal article
• Source: Signal Processing; v. 93, n. 11, p. 3001-3013, NOV 2013.
• Web of Science Citations: 12

Abstract

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)

• FAPESP's process: 11/02655-9 - Analysis of influences of centralized and distributed process scheduling decisions
• Grantee: Rodrigo Fernandes de Mello
• Support Opportunities: Regular Research Grants
• FAPESP's process: 09/18293-9 - A Hybrid Approach to Identify and Model Deterministic and Stochastic Components present in Time Series
• Grantee: Ricardo Araújo Rios
• Support Opportunities: Scholarships in Brazil - Doctorate