Maximum Likelihood Estimation, Interpolation and Prediction for Fractional Brownian Motion

  • Rachid Harba
  • Hassan Douzi
  • Mohamed El Hajji
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7340)

Abstract

The maximum likelihood (ML) estimation approach for fractional Brownian motion (fBm) is explored in this communication. First, a ML based estimation of the H parameter is implemented on the signal itself. This approach on the signal itself can easily be applied on non-uniformly sampled data or directly useful in the case of incomplete data. Secondly, the method is extended to provide a ML prediction and a ML interpolation for fBm which could be of interest in many domains. Results also help to explain errors in other interpolating methods such as the midpoint displacement algorithm used to synthesize fBm data.

Keywords

Fractional Brownian Motion Synthetic Signal Irregular Sampling Fractional Gaussian Noise IEEE Signal Processing Letter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Rachid Harba
    • 1
  • Hassan Douzi
    • 2
  • Mohamed El Hajji
    • 2
  1. 1.Laboratoire PRISME, Polytech’OrléansUniversité d’OrléansOrléansFrance
  2. 2.Laboratoire IRF-SIC, Faculté des sciencesUniversité Ibn ZohrAgadirMorocco

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