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.
install.packages('forecast', dependencies = TRUE)
You can install the development version from Github
# install.packages("remotes") remotes::install_github("robjhyndman/forecast")
library(forecast) library(ggplot2) # ETS forecasts USAccDeaths %>% ets() %>% forecast() %>% autoplot() # Automatic ARIMA forecasts WWWusage %>% auto.arima() %>% forecast(h=20) %>% autoplot() # ARFIMA forecasts library(fracdiff) x <- fracdiff.sim( 100, ma=-.4, d=.3)$series arfima(x) %>% forecast(h=30) %>% autoplot() # Forecasting with STL USAccDeaths %>% stlm(modelfunction=ar) %>% forecast(h=36) %>% autoplot() AirPassengers %>% stlf(lambda=0) %>% autoplot() USAccDeaths %>% stl(s.window='periodic') %>% forecast() %>% autoplot() # TBATS forecasts USAccDeaths %>% tbats() %>% forecast() %>% autoplot() taylor %>% tbats() %>% forecast() %>% autoplot()
For more information
- 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/
This package is free and open source software, licensed under GPL-3.