I will 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 foreasts from ARIMA models, ARFIMA models and exponential smoothing models. I will 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 will be taken from my own consulting experience. I will 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.
Rob J Hyndman is Professor of Statistics at Monash University and Director of the Monash University Business and Economic Forecasting Unit. He completed a science degree at the University of Melbourne in 1988 and a PhD on nonlinear time series modelling at the same university in 1992. He has worked at the University of Melbourne, Colorado State University, the Australian National University and Monash University. Rob is Editor-in-Chief of the “International Journal of Forecasting” and a Director of the International Institute of Forecasters. He has written over 100 research papers in statistical science. In 2007, he received the Moran medal from the Australian Academy of Science for his contributions to statistical research. Rob is co-author of the well-known textbook “Forecasting: methods and applications” (Wiley, 3rd ed., 1998) and of the book “Forecasting with exponential smoothing: the state space approach” (Springer, 2008). He is also the author of the widely-used “forecast” package for R. For over 25 years, Rob has maintained an active consulting practice, assisting hundreds of companies and organizations on forecasting problems. His recent consulting work has involved forecasting electricity demand, tourism demand and the Australian government health budget. More information is available on his website at robjhyndman.com.