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)
# ETS forecasts
fit <- ets(USAccDeaths)
plot(forecast(fit))
# Automatic ARIMA forecasts
fit <- auto.arima(WWWusage)
plot(forecast(fit, h=20))
# ARFIMA forecasts
library(fracdiff)
x <- fracdiff.sim( 100, ma=-.4, d=.3)$series
fit <- arfima(x)
plot(forecast(fit, h=30))
# Forecasting with STL
tsmod <- stlm(USAccDeaths, modelfunction=ar)
plot(forecast(tsmod, h=36))
plot(stlf(AirPassengers, lambda=0))
decomp <- stl(USAccDeaths,s.window="periodic")
plot(forecast(decomp))
# TBATS forecasts
fit <- tbats(USAccDeaths)
plot(forecast(fit))
taylor.fit <- tbats(taylor)
plot(forecast(taylor.fit))
```

## License

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