Introduction to Tidyverts with the Australian retail turnover data
This case study is meant to be a quick introduction to time series analysis in R, using the Tidyverts family of R packages. Tidyverts is the work of Rob Hyndman, professor of statistics at Monash University, and his team. The family is intended to be the next-generation replacement for the very popular forecast package, and is currently under active development.
The main reference for Tidyverts is the textbook Forecasting: Principles and Practice, 3rd Edition, by Hyndman and Athanasopoulos. It’s highly recommended to read that in conjunction with working through the notebooks here.
Summary
The R Notebooks in this directory are as follows. Each notebook also has a corresponding HTML file, which is the rendered output from running the code. This is best viewed on our https://microsoft.github.io/forecasting/ GitHub Page.
01_explore.Rmd(.html)introduces theaus_retaildataset and performs some exploratory analysis, generating graphs and tables.02_model.Rmd(.html)fits a range of simple time series models to the data and discusses the results, including various issues relevant to forecasting in general.
Package installation
The following packages and their dependencies are needed to run the notebooks in this directory:
| Framework | Packages |
|---|---|
| Tidyverse | dplyr, tidyr, ggplot2 |
| Tidyverts | tsibble, tsibbledata, fabletools, fable, feasts |
| Future | future, future.apply |
| Other | urca, rmarkdown |
install.packages("tidyverse") # installs all Tidyverse packages
install.packages(c("future", "future.apply"))
install.packages(c("rmarkdown", "urca"))
install.packages(c("tsibble", "tsibbledata", "fabletools", "fable", "feasts"))
Acknowledgements
Mitchell O’Hara-Wild (@mitchelloharawild) provided many comments that helped improve this example.