Recent advances in computer recording and storing technology have tremendously increased the presence of functional data, whose graphical representation can be infinite-dimensional curve, image, or shape. When the same functional object is observed over a period of time, such data are known as functional time series. This article makes first attempt to describe several techniques (centered around functional principal component analysis) for modeling and forecasting functional time series from a computational aspect, using a readily-available R addon package. These methods are demonstrated using age-specific Australian fertility rate data from 1921 to 2006, and monthly sea surface temperature data from January 1950 to December 2011.
|Number of pages||9|
|Publication status||Published - Jun 2013|