Package: gratis 1.9

Feng Li

gratis: Generating Time Series with Diverse and Controllable Characteristics

Generates synthetic time series based on various univariate time series models including MAR and ARIMA processes. Kang, Y., Hyndman, R.J., Li, F.(2020) <doi:10.1002/sam.11461>.

Authors:Yanfei Kang [aut], Feng Li [aut, cre], Rob Hyndman [aut], Mitchell O'Hara-Wild [ctb], Bocong Zhao [ctb]

gratis_1.9.tar.gz
gratis_1.9.zip(r-4.7-any)gratis_1.9.zip(r-4.6-any)gratis_1.9.zip(r-4.5-any)
gratis_1.9.tgz(r-4.6-any)gratis_1.9.tgz(r-4.5-any)
gratis_1.9.tar.gz(r-4.7-any)gratis_1.9.tar.gz(r-4.6-any)
gratis_1.9.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
gratis/json (API)

# Install 'gratis' in R:

install.packages('gratis', repos = c('https://ykang.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/ykang/gratis/issues

On CRAN:

data-generation mixture-autoregressive statistical-computing time-series

7.55 score 78 stars 45 scripts 494 downloads 12 exports 53 dependencies

Last updated from:0a8ba7e42a. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	169
source / vignettes	OK	305
linux-release	OK	189
macos-release	OK	129
macos-oldrel	OK	174
windows-devel	OK	127
windows-release	OK	152
windows-oldrel	OK	141
wasm-release	OK	126

Exports:arima_model ets_model generate generate_msts generate_target generate_ts generate_ts_with_target mar_model pi_coefficients rmixnorm rmixnorm_ts simulate_target

Dependencies:anytime BH cli codetools colorspace cpp11 crayon digest doRNG dplyr farver foreach forecast fracdiff GA generics ggplot2 glue gtable isoband iterators labeling lattice lifecycle lmtest lubridate magrittr mvtnorm nlme nnet pillar pkgconfig polynom purrr R6 RColorBrewer Rcpp RcppArmadillo rlang rngtools S7 scales tibble tidyselect timechange timeDate tsibble urca utf8 vctrs viridisLite withr zoo

Introduction to gratis

About gratis | Generate diverse time series | Generate multiple seasonal time series | Generate time series with targetted features | ARIMA and ETS models

Rendered fromQuickStart.Rmdusingknitr::rmarkdown

Last update: 2026-02-24
Started: 2020-07-20

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Specify an ARIMA model	arima_model
Specify an ETS model	ets_model
Generate a random multiple-seasonal MAR time series	generate_msts
Generate random MAR time series	generate_ts
Generate time series with target features	generate_ts_with_target
Generate a tsibble of synthetic time series	generate.Arima generate.ets generate.mar
Specify a mixture autoregressive model	mar_model
Compute AR recursion coefficients from SARIMA coefficients	pi_coefficients
Generate random values from a mixture of multivariate normal distributions	rmixnorm
Simulate a mixture-normal autoregressive process	rmixnorm_ts
Generate time series with target features	generate_target simulate_target
Simulate from a specified ARIMA model	simulate.gratis_arima
Simulate from a mixture autoregressive model	simulate.mar