Surrogate Monte Carlo

@article{Silva2021SurrogateMC,
  title={Surrogate Monte Carlo},
  author={A. Christian Silva and Fernando F. Ferreira},
  journal={Machine Learning eJournal},
  year={2021}
}
This article proposes an artificial data generating algorithm that is simple and easy to customize.<br>The fundamental concept is to perform random permutation of Monte Carlo generated random<br>numbers which conform to the unconditional probability distribution of the original real time series.<br>Similar to constraint surrogate methods, random permutations are only accepted if a given objective<br>function is minimized. The objective function is selected in order to describe the most… 

References

SHOWING 1-10 OF 16 REFERENCES

Constrained Randomization of Time Series Data

A new method is introduced to create artificial time sequences that fulfil given constraints but are random otherwise, to avoid certain artifacts generated by Fourier-based randomization schemes.

Practical implementation of nonlinear time series methods: The TISEAN package.

A variety of algorithms for data representation, prediction, noise reduction, dimension and Lyapunov estimation, and nonlinearity testing are discussed with particular emphasis on issues of implementation and choice of parameters.

Brownian Agents and Active Particles: Collective Dynamics in the Natural and Social Sciences

This book demonstrates that Brownian agent models can be successfully applied in many different contexts, ranging from physicochemical pattern formation, to active motion and swarming in biological systems, to self-assembling of networks, evolutionary optimization, urban growth, economic agglomeration and even social systems.

Financial Market Models

The dynamics of financial markets are discussed. After a brief introduction of the price formation process, we review the statistical features (also known as “stylized facts”) of stock return time

Prediction, Estimation, and Attribution

  • B. Efron
  • Computer Science
    International Statistical Review
  • 2020
Several key discrepancies will be examined, centering on the differences between prediction and estimation or prediction and attribution (significance testing), most of the discussion is carried out through small numerical examples.

Computer simulations of Brownian motion of complex systems

Care is needed with algorithms for computer simulations of the Brownian motion of complex systems, such as colloidal and macromolecular systems which have internal degrees of freedom describing changes in configuration, as illustrated by some artificial models.

Folded Empirical Distribution Function Curves—Mountain Plots

Abstract Various graphical methods are available for displaying one or more univariate distributions. In this article the folded empirical distribution function curve, or mountain plot, is described

Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs

This work proposes a Recurrent GAN (RGAN) and Recurrent Conditional GGAN (RCGAN) to produce realistic real-valued multi-dimensional time series, with an emphasis on their application to medical data.

Econophysics review: I. Empirical facts

This article and the companion paper aim at reviewing recent empirical and theoretical developments usually grouped under the term Econophysics. Since the name was coined in 1995 by merging the words