Bayesian Approach to Hurst Exponent Estimation

@article{Dlask2017BayesianAT,
  title={Bayesian Approach to Hurst Exponent Estimation},
  author={Martin Dlask and Jarom{\'i}r Kukal and Oldrich Vysata},
  journal={Methodology and Computing in Applied Probability},
  year={2017},
  volume={19},
  pages={973-983}
}
Fractal investigation of a signal often involves estimating its fractal dimension or Hurst exponent H when considered as a sample of a fractional process. Fractional Gaussian noise (fGn) belongs to the family of self-similar fractional processes and it is dependent on parameter H. There are variety of traditional methods for Hurst exponent estimation. Our novel approach is based on zero-crossing principle and signal segmentation. Thanks to the Bayesian analysis, we present a new axiomatically… CONTINUE READING

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