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anySim is an R package for the stochastic simulation of random variables, stochastic processes and random fields, with any marginal distribution and dependence structure.

The package provides models for the simulation of univariate stationary and cyclostationary processes as well as random fields, exhibiting continuous, discrete and mixed-type marginal distributions and any valid short-range or long-range correlation structure.

Furthermore, it implements a multivariate stationary stochastic model with similar capabilities, preserving cross-dependencies among processes.

Primary use of the package is the generation of synthetic time series and fields (e.g., rainfall, runoff, temperature, wind speed etc.) with the given marginal and stochastic properties.


anySim R package is freely available via:

  • Tsoukalas I., P. Kossieris, and C. Makropoulos (2020) Simulation of non-Gaussian correlated random variables, stochastic processes and random fields: Introducing the anySim R-Package for environmental applications and beyond, Water, 12 (6), 1645, doi:10.3390/w12061645.
  • Kossieris P. (2020) Multi-scale stochastic analysis and modelling of residential water demand processes, PhD thesis, Department of Water Resources and Environmental Engineering – National Technical University of Athens, Athens, February 2020.
  • Kossieris P, Tsoukalas I, Makropoulos C, Savic D (2019) Simulating Marginal and Dependence Behaviour of Water Demand Processes at Any Fine Time Scale. Water 11:885. doi: 10.3390/w11050885
  • Tsoukalas I (2018) Modelling and simulation of non-Gaussian stochastic processes for optimization of water-systems under uncertainty. PhD Thesis, Department of Water Resources and Environmental Engineering, National Technical University of Athens (Defence date: 20 December 2018)
  • Kossieris, P., Makropoulos, C. (2018) Exploring the Statistical and Distributional Properties of Residential Water Demand at Fine Time Scales. Water, 10, 1481.
  • Tsoukalas I, Efstratiadis A, Makropoulos C (2018a) Stochastic Periodic Autoregressive to Anything (SPARTA): Modeling and Simulation of Cyclostationary Processes With Arbitrary Marginal Distributions. Water Resour Res 54:161–185. doi: 10.1002/2017WR021394
  • Tsoukalas I, Efstratiadis A, Makropoulos C (2019) Building a puzzle to solve a riddle: A multi-scale disaggregation approach for multivariate stochastic processes with any marginal distribution and correlation structure. J Hydrol 575:354–380. doi: 10.1016/j.jhydrol.2019.05.017
  • Tsoukalas I, Makropoulos C, Koutsoyiannis D (2018b) Simulation of Stochastic Processes Exhibiting Any-Range Dependence and Arbitrary Marginal Distributions. Water Resour Res 54:9484–9513. doi: 10.1029/2017WR022462
  • Tsoukalas I, Papalexiou S, Efstratiadis A, Makropoulos C (2018c) A Cautionary Note on the Reproduction of Dependencies through Linear Stochastic Models with Non- Gaussian White Noise. Water 10:771. doi: 10.3390/w10060771

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