Hurst exponent estimation from short time series
| Autor: | Martin Dlask, Jaromir Kukal |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Hurst exponent
Work (thermodynamics) Series (mathematics) Continuous modelling 020206 networking & telecommunications 02 engineering and technology Brownian bridge symbols.namesake Quality (physics) Fractal Gaussian noise Signal Processing 0202 electrical engineering electronic engineering information engineering symbols 020201 artificial intelligence & image processing Statistical physics Electrical and Electronic Engineering Mathematics |
| Zdroj: | Signal, Image and Video Processing. 13:263-269 |
| ISSN: | 1863-1711 1863-1703 |
| DOI: | 10.1007/s11760-018-1353-2 |
| Popis: | Fractal investigation of time series is very complex for several reasons. Due to the existence of fully continuous model, on which the majority of conventional methods are based, the quality of Hurst exponent estimate is often influenced by the number of input data and its sampling rate. In this work, we present a novel approach of unbiased Hurst exponent estimate that is suitable especially for short time series. The crucial idea is deriving the discrete fractional Brownian bridge and its statistical properties that can be subsequently used for model parameter estimation. For the verification and demonstration of efficiency of the method, several generators of fractional Gaussian noise are presented and tested. |
| Databáze: | OpenAIRE |
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