forecast

Rob Hyndman’s forcast package is neat. Here is what it does.

library(forecast)

## Loading required package: zoo
## 
## Attaching package: 'zoo'
## 
## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric
## 
## Loading required package: timeDate
## This is forecast 6.1

# 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))

copied from github