I'm trying to get better at using the R statistical computing language and time series analysis. This page will list some useful resources on both topics:

- Rob J Hyndman. Professor of Statistics and author of the forecast R library.
- Forecasting: principles and practice. Online textbook from Rob J Hyndman and George Athanasopoulos.
- Introduction to ARIMA. A powerful technique for modeling a time series.
- How To Identify Patterns in Time Series Data.
- OpenForecast is a library used by many different applications in industry and in accademia.
- An introduction to time series analysis from the engineering statistics handbook.
- An overview of IPredict's time-series forecasting methods.

- Forecast. The R forecast library which can be used with the R time series data structure.
- Machine learning in R presentation by Alexandros Karatzoglou.
*This is a fantastic set of slides that will guide you through some of the basics.* - Clustering in R
*A great guide to clustering with the commands for R.***Warning:**There is an error in the Multi-Gaussian with Expectation-Maximization example.

The line:`plot(mc, data=iris[,1:4], what=c('classification'), dimens=c(3,4))`

*Should be changed to the following in R version 2.15.3:*`plot(mc, what=c('classification'), dimens=c(3,4))`

- Creating publication quality graphs in R.
- Series of tutorials for developers in R by Google