ayotoasset / hurst-exp

Estimating Hurst exponents varying in time using neural networks for time series

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hurst-exp

Estimating Hurst exponents varying in time using neural networks for time series

Ensure you have Python3, tensorflow 1.14 and keras installed. FBM package used is from https://github.com/crflynn/fbm.

model3densediff_n13.h5 is the saved tensorflow model that has been pre-trained

singleexpest.py is the python script to use to classify a single trajectory with a single Hurst exponent.

multiexpest.py is the python script to use to classify a single trajectory with varying Hurst exponent by using a symmetric moving window.

More information can be found on https://www.biorxiv.org/content/10.1101/777615v2 or message/email me (Daniel S Han).

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Estimating Hurst exponents varying in time using neural networks for time series

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