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gratis

The R packagegratis (previously known astsgeneration) providesefficient algorithms for generating time series with diverse andcontrollable characteristics.

Installation

CRAN version

install.packages("gratis")

Development version

You can install thedevelopment version ofgratis package fromGitHub Repository with:

devtools::install_github("ykang/gratis")

Usage

Tutorial video

Watchthis YouTube videoprovided byProf. Rob Hyndman.

Load the package

library(gratis)library(feasts)

Generate diverse time series

set.seed(1)mar_model(seasonal_periods=12) %>%  generate(length=120,nseries=2) %>%  autoplot(value)

Generate mutiple seasonal time series

mar_model(seasonal_periods=c(24,24*7)) %>%  generate(length=24*7*10,nseries=12) %>%  autoplot(value)

Generate time series with controllable features

library(dplyr)# Function to return spectral entropy, and ACF at lags 1 and 2# given a numeric vector inputmy_features<-function(y) {  c(tsfeatures::entropy(y),acf= acf(y,plot=FALSE)$acf[2:3,1,1])}# Produce series with entropy = 0.5, ACF1 = 0.9 and ACF2 = 0.8df<- generate_target(length=60,feature_function=my_features,target= c(0.5,0.9,0.8))df %>% as_tibble() %>% group_by(key) %>% summarise(value= my_features(value),feature=c("entropy","acf1","acf2"),.groups="drop")#> # A tibble: 30 × 3#>    key       value feature#>    <chr>     <dbl> <chr>#>  1 Series 1  0.533 entropy#>  2 Series 1  0.850 acf1#>  3 Series 1  0.735 acf2#>  4 Series 10 0.478 entropy#>  5 Series 10 0.880 acf1#>  6 Series 10 0.764 acf2#>  7 Series 2  0.507 entropy#>  8 Series 2  0.890 acf1#>  9 Series 2  0.899 acf2#> 10 Series 3  0.454 entropy#> # … with 20 more rowsautoplot(df)

Web application

You can also run the time series generation procedure in a shiny app

app_gratis()

Or visit ouronline ShinyAPP

References

Kang, Y., Hyndman, R., and Li, F. (2020).GRATIS:GeneRAtingTImeSeries with diverse and controllablecharacteristics.Statistical Analysis and DataMining.

License

This package is free and open source software, licensed under GPL-3.

Acknowledgements

Feng Li and Yanfei Kang are supported by the National Natural ScienceFoundation of China (No. 11501587 and No. 11701022 respectively). Rob JHyndman is supported by the Australian Centre of Excellence inMathematical and Statistical Frontiers.