Unlike most time series studied in statistics, chaotic time series
generally contain very little noise, are highly nonlinear, and are
very long (1,000-100,000 points). For prediction, researchers have
often used nonlinear regression algorithms such as radial basis
functions, neural networks, MARS, and k-nearest neighbors regression
(loess). However, there are several important differences between the
regression problem and the time series prediction problem that have
been mostly overlooked. A new local modeling algorithm will be
described that incorporates these differences to increase accuracy and
reduce computation.
Date: Wed., May 27; Time: 4:15-5:30PM; Place: Cordura 100
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