The R package `gratis`

(previously known as `tsgeneration`

) provides efficient algorithms for generating time series with diverse and controllable characteristics.

You can install the **development** version of `gratis`

package from GitHub Repository with:

Watch this YouTube video provided by Prof. Rob Hyndman.

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

```
library(dplyr)
# Function to return spectral entropy, and ACF at lags 1 and 2
# given a numeric vector input
my_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 = 0.8
df <- 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 rows
autoplot(df)
```

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

Or visit our online Shiny APP

- R package
`tsfeatures`

from GitHub Repository.

- Kang, Y., Hyndman, R., and Li, F. (2020).
**GRATIS**:**G**ene**RA**ting**TI**me**S**eries with diverse and controllable characteristics. Statistical Analysis and Data Mining.

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

Feng Li and Yanfei Kang are supported by the National Natural Science Foundation of China (No. 11501587 and No. 11701022 respectively). Rob J Hyndman is supported by the Australian Centre of Excellence in Mathematical and Statistical Frontiers.