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Optimizing the detection of nonstationary signals by using recurrence analysis
Thiago de Lima Prado1, Gustavo Zampier Dos Santos Lima2, Bruno Lobão-Soares3
1Instituto de Engenharia, Ciência e Tecnologia, Universidade Federal dos Vales do Jequitinhonha e Mucuri, 39.440-000 Janaúa, Brazil.
Chaos (Woodbury, N.Y.)
|September 6, 2018
Summary
This study introduces an optimized recurrence analysis method to enhance sensitivity for detecting subtle dynamic changes in time series. This advanced technique improves the prediction of future system states from current observational data.
Area of Science:
- Dynamical systems analysis
- Nonlinear time series analysis
- Physiological signal processing
Background:
- Recurrence analysis is sensitive to the vicinity threshold parameter, impacting its ability to detect dynamic changes.
- Optimizing this threshold is crucial for enhancing the sensitivity of recurrence quantifiers.
Purpose of the Study:
- To develop an optimized vicinity threshold evaluation for recurrence analysis.
- To increase the sensitivity of recurrence quantifiers for detecting small dynamical variations.
- To establish recurrence analysis as a predictive tool for nonstationary time series.
Main Methods:
- Development of a novel method for optimizing the vicinity threshold in recurrence analysis.
- Application of the enhanced recurrence analysis to numerically generated time series.
- Validation using experimental physiological data.
Main Results:
- The optimized method significantly enhances the sensitivity of recurrence analysis to small signal variations.
- The improved technique allows for the detection of nonstationary behavior in time signals and space profiles.
- The approach demonstrates potential for predicting future system states based on current data.
Conclusions:
- Optimized recurrence analysis offers a more sensitive approach to detecting subtle changes in dynamical systems.
- This method can provide insights into the current state and predict future states of physical processes.
- The technique shows promise as a precursor for near-future state prediction in various systems.
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