Determining the predictability of signals

Research output: Conference proceeding/Chapter in Book/Report/Conference Paperpeer-review

Abstract

In case of signal or time series prediction, it is important to know if there is any chance for prediction or not. Therefore, the maximum achievable prediction gain is the desired measure used to characterize the future knowledge of a signal. We present a method to evaluate the maximum prediction gain based on the observed signal only. Hence, the presented method does not rely on a special prediction function, therefore it is suitable for a decision whether any given predictor is good enough or could be improved. To aid system identification tasks the progress of the prediction gain is used as an additional model selection rule. Considering different signal types the predictability behaves differently, i.e., it keeps constant; for periodic signals or vanishes in the case of chaotic or random signals.
Original languageEnglish
Title of host publication1996 IEEE Digital Signal Processing Workshop Proceedings
PublisherIEEE Computer Society
Pages291-294
Number of pages4
ISBN (Print)0-7803-3629-1
DOIs
Publication statusPublished - 4 Sept 1996
Externally publishedYes
Event1996 IEEE Digital Signal Processing Workshop Proceedings - Loen, Norway
Duration: 1 Sept 19964 Sept 1996

Publication series

Name1996 IEEE Digital Signal Processing Workshop Proceedings

Conference

Conference1996 IEEE Digital Signal Processing Workshop Proceedings
Period1/09/964/09/96

Keywords

  • Predictive models
  • System identification
  • Chaotic communication
  • Noise measurement
  • Radio frequency
  • Upper bound

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