Search research articles

Search research articles

Related Experiment Videos

Reducing noise in discretized time series.

M C Cuéllar¹, P M Binder

¹Departamento de Física, Universidad de Los Andes, Apartado Aéreo 4976, Bogotá, Colombia.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|November 3, 2001

Summary

This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Rhythmic natural selection over intertidal and brackish water genotypes: simple formulations for testing hypothesis.

Marine genomics·2014

Same author

Finite statistical complexity for sofic systems.

Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics·2002

Same author

Multiscale analysis of complex systems.

Physical review. E, Statistical, nonlinear, and soft matter physics·2001

See all related articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Applying noise reduction to discretized time series data increases error. Adding external noise before reduction mitigates this error increase and improves Lyapunov exponent estimation.

Area of Science:

Data analysis
Time series analysis
Signal processing

Background:

Discretization of continuous time series data is common in scientific analysis.
Noise reduction algorithms are frequently applied to processed data.
The impact of these algorithms on error propagation is not fully understood.

Purpose of the Study:

To investigate the effect of noise reduction algorithms on discretized time series.
To explore methods for mitigating error increases caused by noise reduction.
To assess the impact on the estimation of dynamical system properties, such as Lyapunov exponents.

Main Methods:

Comparison of average error between original and noise-reduced discretized time series.
Introduction of controlled external noise prior to applying the noise reduction algorithm.

Related Experiment Videos

Evaluation of Lyapunov exponent estimation accuracy before and after the proposed noise addition and reduction process.

Main Results:

Applying a noise-reduction algorithm to a discretized time series demonstrably increases its average error compared to the original series.
Introducing external noise, equivalent in magnitude to the discretization step, before noise reduction significantly limits the error increase.
This noise-addition strategy also enhances the accuracy of Lyapunov exponent estimation.

Conclusions:

Standard noise reduction on discretized time series can amplify errors.
A specific pre-processing step involving adding calibrated external noise can counteract these errors.
This refined approach improves the reliability of analyzing dynamical systems from discretized data.