Forecasters use all kinds of software to do their job; software that comes integrated with enterprise software, specialised statistical packages, or even Excel. Here is a small instruction how to forecast with the open source statistical environment R.

**What is R?**

R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R.

R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible.

**Install the R software**

To install R dowload the latest precompiled binary base from a location near you via:

http://cran.r-project.org/mirrors.html

After download completes, run the setup program for a full install. After this you should be able to start R from the Start Menu in Windows, or it’s corresponding location if you use another operating system.

**Install the forecast package**

The next step is to install the forecast library. To do so you should select a mirror from the GUI menu at:

`Packages > Set CRAN mirror`

Select a location nearby to speed up downloads. Next, select the forecast package from the menu “Packages > Install package(s)”. The list is alphabetical so you should scroll down a bit. Select forecast, press OK and the package will install itself and all packages it needs.

Before using the forecast package, you should load it into the current R workspace. To do so, go to the menu “Packages > Load package” and select forecast again. Press OK and it will load the forecast and all required packages. An other way is to type in the console:

`library(forecast)`

**Import data into R**

The next step is to import data and forecast. R has many import functions for files, other statistical programs and databases. We use a simple comma seperated file here, but if you have another source of data, take a look at the manual at:

http://cran.r-project.org/doc/manuals/R-data.pdf

To import data from a csv file, you can use the read.csv() function. If the file is named “mydata.csv”, has headings and is seperated with commas, the command to type in the console is:

Data1 <- read.csv(file="mydata.csv", head="TRUE", sep=",")

Next step is to define a time series, for example based on a column with name Col1:

Series1 <- ts(Data1$Col1, frequency=12, start=2009)

**Fit a model and forecast**

The forecast package has a automatic exponential smoothing algorithm that delivers great performance. Although there is a lot of computation involved, it can be handled remarkably quickly on modern computers.

To fit a model to the time series:

fit <- ets(Series1)

Display a summary of the fitted model:

summary(fit)

Plot a graph of the forecast 4 periods out:

plot(forecast(fit,h=4))

**More information**

To read more on the forecast package for R:

http://www.jstatsoft.org/v27/i03/paper

The author of the R forecast package:

http://robjhyndman.com/