Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Towards Generic Simulation for Demanding Stochastic Processes

Version 1 : Received: 25 May 2021 / Approved: 26 May 2021 / Online: 26 May 2021 (08:12:48 CEST)

A peer-reviewed article of this Preprint also exists.

Koutsoyiannis, D.; Dimitriadis, P. Towards Generic Simulation for Demanding Stochastic Processes. Sci 2021, 3, 34. Koutsoyiannis, D.; Dimitriadis, P. Towards Generic Simulation for Demanding Stochastic Processes. Sci 2021, 3, 34.


We outline and test a new methodology for genuine simulation of stochastic processes with any dependence and any marginal distribution. We reproduce time dependence with a generalized, time symmetric or asymmetric, moving-average scheme. This implements linear filtering of non-Gaussian white noise, with the weights of the filter determined by analytical equations in terms of the autocovariance of the process. We approximate the marginal distribution of the process, irrespective of its type, using a number of its cumulants, which in turn determine the cumulants of white noise in a manner that can readily support the generation of random numbers from that approximation, so that it be applicable for stochastic simulation. The simulation method is genuine as it uses the process of interest directly without any transformation (e.g. normalization). We illustrate the method in a number of synthetic and real-world applications with either persistence or antipersistence, and with non-Gaussian marginal distributions that are bounded, thus making the problem more demanding. These include distributions bounded from both sides, such as uniform, and bounded form below, such as exponential and Pareto, possibly having a discontinuity at the origin (intermittence). All examples studied show the satisfactory performance of the method.


stochastics; stochastic processes; stochastic simulation; Monte Carlo simulation; long range de-pendence; persistence; Hurst-Kolmogorov dynamics; climacogram; cumulants; intermittence


Computer Science and Mathematics, Probability and Statistics

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