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Using the minimum description length principle for global reconstruction of dynamic systems from noisy time series
Ya I Molkov1, D N Mukhin, E M Loskutov
1Institute of Applied Physics, Russian Academy of Sciences, Nizhny Novgorod, Russia.
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|November 13, 2009
Summary
This study introduces a novel artificial neural network approach for determining embedding dimension in noisy time series. This method proves more robust to noise than traditional techniques, aiding in dynamic and stochastic system reconstruction.
Area of Science:
- Dynamical Systems Theory
- Computational Neuroscience
- Machine Learning
Background:
- Reconstructing dynamic systems from noisy time series is crucial for understanding complex phenomena.
- Existing methods for determining embedding dimension, such as the false nearest-neighbor method, are often inefficient and sensitive to noise.
Purpose of the Study:
- To propose an alternative, noise-resilient approach for determining the embedding dimension.
- To enhance the accuracy of dynamic system reconstruction from time series data.
Main Methods:
- Development of a global model using an artificial neural network.
- Optimization of the number of neurons and embedding dimension for minimal description length.
- Evaluation of the method's sensitivity to noise levels and origins.
Main Results:
- The proposed artificial neural network approach is significantly less sensitive to noise compared to traditional methods.
- The method effectively determines embedding dimension even with low noise levels.
- Successful application in reconstructing both dynamic and stochastic models.
Conclusions:
- Artificial neural networks offer a powerful alternative for determining embedding dimension in noisy time series.
- This approach improves the reliability of dynamic system reconstruction.
- The method is a valuable tool for analyzing stochastic systems.
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