The R package forecast provides methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling.
A complementary forecasting package is the fable package, which implements many of the same models but in a tidyverse framework.
You can install the stable version from CRAN.
('forecast', dependencies = TRUE)install.packages
You can install the development version from Github
# install.packages("remotes") remotes::install_github("robjhyndman/forecast")
(forecast) library(ggplot2) library # ETS forecasts %>% USAccDeaths () %>% ets() %>% forecast() autoplot # Automatic ARIMA forecasts %>% WWWusage () %>% auto.arima(h=20) %>% forecast() autoplot # ARFIMA forecasts (fracdiff) library<- fracdiff.sim( 100, ma=-.4, d=.3)$series x (x) %>% arfima(h=30) %>% forecast() autoplot # Forecasting with STL %>% USAccDeaths (modelfunction=ar) %>% stlm(h=36) %>% forecast() autoplot %>% AirPassengers (lambda=0) %>% stlf() autoplot %>% USAccDeaths (s.window='periodic') %>% stl() %>% forecast() autoplot # TBATS forecasts %>% USAccDeaths () %>% tbats() %>% forecast() autoplot %>% taylor () %>% tbats() %>% forecast() autoplot
- Get started in forecasting with the online textbook at http://OTexts.org/fpp2/
- Read the Hyndsight blog at https://robjhyndman.com/hyndsight/
- Ask forecasting questions on http://stats.stackexchange.com/tags/forecasting
- Ask R questions on http://stackoverflow.com/tags/forecasting+r
- Join the International Institute of Forecasters: http://forecasters.org/