Generate Synthetic Data from Statistical Models.

synthesis: Synthetic data generator

Generate synthetic time series from commonly used statistical models, including linear, nonlinear and chaotic systems. Applications to testing methods can be found in Jiang, Z., Sharma, A., & Johnson, F. (2019) \doi{10.1016/j.advwatres.2019.103430} and Jiang, Z., Sharma, A., & Johnson, F. (2020) \doi{10.1029/2019WR026962} associated with an open-source tool by Jiang, Z., Rashid, M. M., Johnson, F., & Sharma, A. (2020) \doi{10.1016/j.envsoft.2020.104907}.

It can be used for variable selection, prediction, and classification and clustering problem generation.

Requirements

Dependencies:
  stats, MASS

Suggest:
  testthat, devtools

Installation

You can install the package via devtools from GitHub with:

devtools::install_github("zejiang-unsw/synthesis")

or via CRAN with:

install.packages("synthesis")

Report issues

https://github.com/zejiang-unsw/synthesis/issues

Update history

synthesis 1.2.2

• Included the affine error model.
• Included the Brownian motion models.
• Included the build-up and wash-off water quality model.
• Updated the vignette.

synthesis 1.2.1

• Added a vignette to the package.
• Included the state-space model.

synthesis 1.2.0

• Added a NEWS.md file to track changes to the package.
• Initial version submitted to CRAN.

Citation

Jiang, Z., Rashid, M. M., Johnson, F., & Sharma, A. (2020). A wavelet-based tool to modulate variance in predictors: An application to predicting drought anomalies. Environmental modelling & software, 135, 104907.

Jiang, Z., Sharma, A., & Johnson, F. (2020). Refining Predictor Spectral Representation Using Wavelet Theory for Improved Natural System Modeling. Water Resources Research, 56(3), e2019WR026962.

Jiang, Z., Sharma, A., & Johnson, F. (2019). Assessing the sensitivity of hydro-climatological change detection methods to model uncertainty and bias. Advances in Water Resources, 134, 103430.

Galelli, S., Humphrey, G. B., Maier, H. R., Castelletti, A., Dandy, G. C., & Gibbs, M. S. (2014). An evaluation framework for input variable selection algorithms for environmental data-driven models. Environmental modelling & software, 62, 33-51.

Sharma, A. (2000). Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: Part 1 - A strategy for system predictor identification. Journal of Hydrology, 239(1), 232-239.