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Related Experiment Videos

Analysis of time series from stochastic processes

Gradisek1, Siegert, Friedrich

  • 1Faculty of Mechanical Engineering, University of Ljubljana, Askerceva 6, SI-1000 Ljubljana, Slovenia.

Physical Review. E, Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics
|November 23, 2000
PubMed
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This study introduces a method to analyze time series data from stochastic processes using Langevin equations. It enables direct estimation of drift and diffusion coefficients for various applications, including metal cutting experiments.

Area of Science:

  • Stochastic processes
  • Nonlinear dynamics
  • Data analysis

Background:

  • Stochastic processes are fundamental in many scientific fields.
  • Analyzing time series data from these processes is crucial.
  • Langevin and Fokker-Planck equations model such systems.

Purpose of the Study:

  • To develop a method for analyzing time series data from stochastic processes.
  • To estimate drift and diffusion coefficients directly from time series.
  • To propose applications for this analysis.

Main Methods:

  • Analysis of time series data.
  • Estimation of drift and diffusion coefficients.
  • Application of Langevin and Fokker-Planck equations.

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Main Results:

  • Direct estimation of drift and diffusion coefficients from time series is feasible.
  • The proposed method is applicable to both synthetic and experimental data.
  • Successful illustration using metal cutting experimental data.

Conclusions:

  • The developed method provides a direct approach to analyze stochastic processes.
  • The estimation of coefficients from time series has broad applicability.
  • This technique is valuable for understanding complex systems like those in metal cutting.