Nonparametric Learning of Switching Autoregressive Processes

author: Emily Fox, Department of Statistics, University of Washington
published: Aug. 4, 2008,   recorded: July 2008,   views: 691
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Description

Vector autoregressive (VAR) processes are useful in describing dynamical phenomena as diverse as speech, financial time-series, and the dancing of honey bees. However, such phenomena often exhibit structural changes over time and the VAR which describe them must also change. For example, the vocal tract of a speaker contracts; a country experiences a recession, a central bank intervention, or some national or global event; a honey bee changes from a waggle to a turn right dance. Some of these changes will appear fre- quently, while others are only rarely observed. In ad- dition, there is always the possibility of a previously unseen dynamic behavior. Thus, we propose a non- parametric approach for learning switching VAR pro- cesses, where we take the state sequence to be Markov....

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Download slides icon Download slides: icml08_fox_nls_01.pdf (1.7 MB)

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Reviews and comments:

Comment1 student, May 2, 2009 at 5:07 a.m.:

The sound is terrible but the presentation is solid. Nice work Emily.

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