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Adapting to Non-stationarity with Growing Expert Ensembles

Authors
  • Shalizi, Cosma Rohilla
  • Jacobs, Abigail Z.
  • Klinkner, Kristina Lisa
  • Clauset, Aaron
Type
Preprint
Publication Date
Jun 28, 2011
Submission Date
Mar 04, 2011
Source
arXiv
License
Yellow
External links

Abstract

When dealing with time series with complex non-stationarities, low retrospective regret on individual realizations is a more appropriate goal than low prospective risk in expectation. Online learning algorithms provide powerful guarantees of this form, and have often been proposed for use with non-stationary processes because of their ability to switch between different forecasters or ``experts''. However, existing methods assume that the set of experts whose forecasts are to be combined are all given at the start, which is not plausible when dealing with a genuinely historical or evolutionary system. We show how to modify the ``fixed shares'' algorithm for tracking the best expert to cope with a steadily growing set of experts, obtained by fitting new models to new data as it becomes available, and obtain regret bounds for the growing ensemble.

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