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.
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()
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