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

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 seco...

Full description

Saved in:
Bibliographic Details
Published in:Signal processing 2013-11, Vol.93 (11), p.3001-3013
Main Authors: Rios, Ricardo Araújo, de Mello, Rodrigo Fernandes
Format: Article
Language:English
Subjects:
Adjustment
Applied sciences
Decomposition
Detection, estimation, filtering, equalization, prediction
Deterministic component
Empirical analysis
Empirical mode decomposition
Exact sciences and technology
Information, signal and communications theory
Recurrence plot
Signal and communications theory
Signal processing
Signal, noise
Stochastic component
Stochasticity
Telecommunications and information theory
Time series
Time series analysis
Time series decomposition
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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. •We present an approach to decompose time series into components.•The obtained components allow understanding stochastic and deterministic influences.•By knowing such influences we can estimate models with high accuracy.•A new measure evaluates the behavior of the predicted and expected observations.•Experiments confirmed improvements by modeling stochastic/deterministic influences.
ISSN:0165-1684
1872-7557
DOI:10.1016/j.sigpro.2013.04.017