hurst-estimators 0.0.3

Library for Hurst estimation methods

Hurst Estimators

Hurst Estimators is a Python library for estimating the Hurst exponent of time series data using various methods. This library includes implementations of several popular Hurst exponent estimation methods, as well as utilities for generating synthetic data and analyzing results.

Installation

You can install the library using pip:

pip install hurst-estimators

or clone from the github repo:

git clone https://github.com/edesaras/hurst-estimators.git

Importing the library

import hurst_estimators as he

Available Methods

Time Domain Estimators

• Central Estimator
• Detrended Fluctuation Estimator
• General Hurst Exponent Estimator
• Higuchi Estimator
• Rescaled Range Estimator

Frequency Domain Estimators

• Periodogram Estimator

Wavelet Estimators

• Average Wavelet Coefficient Estimator
• Variance Versus Level Wavelet Estimator

Simulators

• Fractional Gaussian Noise (Circulant Embedding Method)

Quick Example

Contributing

Citation

@software{hurst_estimators,
  author = {Aras Edes},
  title = {hurst-estimators: A Python library for Hurst exponent estimation},
  year = {2024},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/edesaras/hurst-estimators}},
}