Generate Synthetic Data from Statistical Models
Generate predictor and response data from AR1 model.
Generate predictor and response data from AR4 model.
Generate predictor and response data from AR9 model.
Generate an affine error model.
Gaussian Blobs
Generate a time series of Brownian motion.
Generate build-up and wash-off model for water quality modeling
Circles
Duffing map
Generate a time series of fractional Brownian motion.
Friedman with independent uniform variates
Friedman with correlated uniform variates
Generate a time series of geometric Brownian motion.
Henon map
Generate predictor and response data: Hysteresis Loop
Linear Gaussian state-space model
Logistic map
Lorenz system
Nonlinear system with independent/correlate covariates
Nonlinear system with Exogenous covariates
Generate correlated normal variates
Rössler system
Generate Random walk time series.
Spirals
Generate predictor and response data: Sinusoidal model
Generate a two-regime threshold autoregressive (TAR) process.
Generate predictor and response data from TAR1 model.
Generate predictor and response data from TAR2 model.
Generate correlated uniform variates
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>.
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