Hurst exponent analysis hurst
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sure! hurst exponent analysis is a statistical method used to quantify the long-term memory of a time series data, indicating whether the data shows a tendency to exhibit persistent trends or mean reversion. the hurst exponent, denoted as h, typically ranges from 0 to 1, where:
h 0.5 indicates mean reversion (anti-persistence),
h = 0.5 indicates a random walk (no memory),
h 0.5 indicates long-term memory (trend persistence).
you can calculate the hurst exponent using the rescaled range (r/s) analysis method. here is a step-by-step guide along with a code example in python using the `numpy` library:
1. import the necessary libraries:
2. define a function to compute the hurst exponent using the r/s analysis method:
3. generate a synthetic time series data for demonstration purposes:
4. calculate the hurst exponent for the generated data:
by following these steps and running the code example, you can calculate the hurst exponent for a given time series data using the r/s analysis method in python. feel free to experiment with different data sets and window sizes to gain a better understanding of the concept.
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