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.
This package is now retired in favour of the fable package. The forecast package will remain in its current state, and maintained with bug fixes only. For the latest features and development, we recommend forecasting with the fable package.
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