Generate Synthetic Data from Statistical Models

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) and Jiang, Z., Sharma, A., & Johnson, F. (2020) associated with an open-source tool by Jiang, Z., Rashid, M. M., Johnson, F., & Sharma, A. (2020) .

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.