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The R packagefable provides a collection of commonly used univariateand multivariate time series forecasting models including exponentialsmoothing via state space models and automatic ARIMA modelling. Thesemodels work within the fable framework, which provides the tools toevaluate, visualise, and combine models in a workflow consistent withthe tidyverse.

You can install thestable version fromCRAN:

install.packages("fable")

You can install thedevelopment version fromGitHub

# install.packages("remotes")remotes::install_github("tidyverts/fable")

Installing this software requires a compiler

library(fable)library(tsibble)library(tsibbledata)library(lubridate)library(dplyr)aus_retail %>%  filter(State%in% c("New South Wales","Victoria"),Industry=="Department stores"  ) %>%   model(ets= ETS(box_cox(Turnover,0.3)),arima= ARIMA(log(Turnover)),snaive= SNAIVE(Turnover)  ) %>%  forecast(h="2 years") %>%   autoplot(filter(aus_retail, year(Month)>2010),level=NULL)

• The pkgdown site describes all models provided by fable, and how theyare used:https://fable.tidyverts.org/
• The forecasting principles and practices online textbook provides anintroduction to time series forecasting using fable:https://otexts.com/fpp3/ (WIP)

• Questions about forecasting can be asked onCrossValidated.

• Common questions about the fable package are often found onStackOverflow. You can use this toask for help if the question isn’t already answered. Aminimallyreproducible example that describesyour issue is the best way to ask for help!