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Intermittency in predicting the behavior of stochastic systems using reservoir computing.

Nikita Kulagin1, Andrey Andreev1, Alexey A Koronovskii2

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Researchers observed a novel intermittent behavior in reservoir computing models predicting stochastic systems. This behavior, akin to on-off intermittency, occurs near critical parameter values, impacting prediction accuracy.

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Area of Science:

  • Complex Systems
  • Computational Physics
  • Nonlinear Dynamics

Background:

  • Reservoir computing models are used for predicting complex system dynamics.
  • Stochastic systems exhibit inherent randomness, posing prediction challenges.
  • Understanding model behavior near critical parameter values is crucial for accurate predictions.

Purpose of the Study:

  • To identify and characterize novel behaviors in reservoir computing models for stochastic systems.
  • To investigate the phenomenon of intermittency in prediction accuracy.
  • To propose a new metric for quantifying prediction quality.

Main Methods:

  • Utilizing reservoir computing models to predict the dynamics of stochastic systems.
  • Systematically varying control parameters of both the stochastic system and the reservoir model.
  • Observing and analyzing prediction accuracy, particularly near threshold parameter values.
  • Characterizing intermittent prediction behavior and comparing it to known intermittency types.

Main Results:

  • A new intermittent behavior type was observed in reservoir computing predictions of stochastic systems.
  • This behavior, termed 'on-off intermittency', manifests as alternating intervals of accurate and inaccurate predictions near critical parameter thresholds.
  • The characteristics of the observed intermittency align with established on-off intermittency phenomena.
  • A concept of 'effective noise' was proposed to describe prediction quality, with a method for estimating its amplitude developed.

Conclusions:

  • Reservoir computing models exhibit 'on-off intermittency' when predicting stochastic systems near critical parameter values.
  • The 'effective noise' concept provides a novel way to quantify prediction quality in such scenarios.
  • The developed estimation technique for effective noise amplitude offers a tool for assessing and potentially improving prediction performance.