#forecast

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.

Installation

You can install the stable version on R CRAN.

install.packages('forecast', dependencies = TRUE)

You can install the development version from Github

# install.packages("devtools")
devtools::install_github("robjhyndman/forecast")

Usage

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/fpp/
• Read the Hyndsight blog at http://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/

License

This package is free and open source software, licensed under GPL (>= 2).