CoSMoS-package: CoSMoS: Complete Stochastic Modelling Solution

In TycheLab/CoSMoS: Complete Stochastic Modelling Solution

Description

CoSMoS is an R package that makes time series generation with desired properties easy. Just choose the characteristics of the time series you want to generate, and it will do the rest.

Details

The generated time series preserve any probability distribution and any linear autocorrelation structure. Users can generate as many and as long time series from processes such as precipitation, wind, temperature, relative humidity etc. It is based on a framework that unified, extended, and improved a modelling strategy that generates time series by transforming "parent" Gaussian time series having specific characteristics (Papalexiou, 2018).

Funding

The package was partly funded by the Global institute for Water Security (GIWS; https://water.usask.ca/) and the Global Water Futures (GWF; https://gwf.usask.ca/) program.

Author(s)

Coded by: Filip Strnad [e-mail] and Francesco Serinaldi [e-mail]

Conceptual design by: Simon Michael Papalexiou [e-mail]

Tested and documented by: Yannis Markonis [e-mail]

Maintained by: Kevin Shook [e-mail]

References

Papalexiou, S.M. (2018). Unified theory for stochastic modelling of hydroclimatic processes: Preserving marginal distributions, correlation structures, and intermittency. Advances in Water Resources 115, 234-252, doi: 10.1016/j.advwatres.2018.02.013

Papalexiou, S.M., Markonis, Y., Lombardo, F., AghaKouchak, A., Foufoula-Georgiou, E. (2018). Precise Temporal Disaggregation Preserving Marginals and Correlations (DiPMaC) for Stationary and Nonstationary Processes. Water Resources Research, 54(10), 7435-7458, doi: 10.1029/2018WR022726

Papalexiou, S.M., Serinaldi, F. (2020). Random Fields Simplified: Preserving Marginal Distributions, Correlations, and Intermittency, With Applications From Rainfall to Humidity. Water Resources Research, 56(2), e2019WR026331, doi: 10.1029/2019WR026331

Papalexiou, S.M., Serinaldi, F., Porcu, E. (2021). Advancing Space-Time Simulation of Random Fields: From Storms to Cyclones and Beyond. Water Resources Research, 57, e2020WR029466, doi: 10.1029/2020WR029466

TycheLab/CoSMoS documentation built on June 6, 2021, 2:35 a.m.