forecast <img src="man/figures/logo.png" align="right" />
======================

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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](http://fable.tidyverts.org/) package, which implements many of the same models but in a tidyverse framework.

## Installation
You can install the **stable** version from
[CRAN](https://cran.r-project.org/package=forecast).

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

You can install the **development** version from
[Github](https://github.com/robjhyndman/forecast)

```s
# install.packages("remotes")
remotes::install_github("robjhyndman/forecast")
```

## Usage

```s
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/

## License

This package is free and open source software, licensed under GPL-3.
