I gave this talk on Forecasting time series using R for the Melbourne Users of R Network (MelbURN) on Thursday 27 October 2011.
Abstract
I look at the various facilities for time series forecasting available in R, concentrating on the forecast package. This package implements several automatic methods for forecasting time series including forecasts from ARIMA models, ARFIMA models and exponential smoothing models. I also look more generally at how to go about forecasting non-seasonal data, seasonal data, seasonal data with high frequency, and seasonal data with multiple frequencies. Examples are taken from my own consulting experience. I give an overview of what’s possible and available and where it is useful, rather than give the mathematical details of any specific time series methods.
Related Posts:
- Forecasting with long seasonal periods
- Initializing the Holt-Winters method
- Different results from different software
- Cyclic and seasonal time series
- Forecasting workshop: Switzerland, June 2011
Rob J Hyndman