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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
tsfeatures
from GitHub Repository.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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.