Universal piecewise linear least squares prediction

David Luengo, Suleyman S. Kozat, Andrew Carl Singer

Research output: Contribution to journalConference article

Abstract

We consider the problem of sequential prediction of real-valued sequences using piece-wise linear models under the square-error loss function. In this context, we demonstrate a sequential algorithm for prediction whose accumulated squared error for every bounded sequence is asymptotically as small as that of the best fixed predictor for that sequence taken from the class of piecewise linear predictors. We also show that this predictor is optimal in certain settings in a particular min-max sense. This approach can also be applied to the class of piecewise constant predictors, for which a similar universal sequential algorithm can be derived with corresponding min-max optimality.

Original languageEnglish (US)
Number of pages1
JournalIEEE International Symposium on Information Theory - Proceedings
StatePublished - Oct 20 2004
EventProceedings - 2004 IEEE International Symposium on Information Theory - Chicago, IL, United States
Duration: Jun 27 2004Jul 2 2004

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics

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