Universal linear least-squares prediction in the presence of noise

Georg C. Zeitler, Andrew Carl Singer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Universal linear least squares prediction of real-valued bounded individual sequences in the presence of additive bounded noise is considered. It is shown that there is a sequential predictor observing noisy samples of the sequence to be predicted only, whose loss in terms of the noise-free sequence is asymptotically as small as that of the best batch predictor out of the class of all linear predictors with knowledge of the entire noisy sequence in advance.

Original languageEnglish (US)
Title of host publication2007 IEEE/SP 14th Workshop on Statistical Signal Processing, SSP 2007, Proceedings
Pages611-614
Number of pages4
DOIs
StatePublished - 2007
Event2007 IEEE/SP 14th WorkShoP on Statistical Signal Processing, SSP 2007 - Madison, WI, United States
Duration: Aug 26 2007Aug 29 2007

Other

Other2007 IEEE/SP 14th WorkShoP on Statistical Signal Processing, SSP 2007
CountryUnited States
CityMadison, WI
Period8/26/078/29/07

Keywords

  • Least squares
  • Linear
  • Noise
  • Prediction

ASJC Scopus subject areas

  • Signal Processing

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