knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "100%" )
The R package gratis
(previously known as tsgeneration
) provides efficient algorithms for generating time series with
diverse and controllable characteristics.
install.packages("gratis")
You can install the development version of gratis
package from GitHub
Repository with:
devtools::install_github("ykang/gratis")
Watch this Youtube video provided by Prof. Rob Hyndman.
library(gratis) library(feasts)
set.seed(1) mar_model(seasonal_periods=12) %>% generate(length=120, nseries=2) %>% autoplot(value)
mar_model(seasonal_periods=c(24, 24*7)) %>% generate(length=24*7*10, nseries=12) %>% autoplot(value)
library(dplyr) # Function to return spectral entropy, and ACF at lags 1 and 2 # given a numeric vector input my_features <- function(y) { c(tsfeatures::entropy(y), acf = acf(y, plot = FALSE)$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") autoplot(df)
You can also run the time series generation procedure in a shiny app
app_gratis()
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.
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