Signal Processing Stack Exchange is a question and answer site for practitioners of the art and science of signal, image and video processing. It's 100% free, no registration required.

Sign up
Here's how it works:
  1. Anybody can ask a question
  2. Anybody can answer
  3. The best answers are voted up and rise to the top

Are there any general assumptions for the calculation of the Hurst exponent?

  • Does the signal need to be stationary, for example?

  • Does it depend on the method?

  • What about the length of the time series, is longer better?

I am interested in using implementing it for the analysis of an EEG signal. I have read many articles but can't find answers for these questions.

share|improve this question
up vote 2 down vote accepted

The best is to calculate Hurst for stationary data (as then you can somehow find out if there are long range dependency signals which are not part of AR or MAs) but you can do non stationary as well. You can calculated froma fractal or wavelet transforms. Results are different

In calculating Hurst you have to divide the data in short windows and average the results, (place a range the last 8 elements). Hurst can fluctuate significantly depending on the period you select.

share|improve this answer

Your Answer


By posting your answer, you agree to the privacy policy and terms of service.

Not the answer you're looking for? Browse other questions tagged or ask your own question.