--- a
+++ b/pyeeg/hurst.py
@@ -0,0 +1,60 @@
+import numpy
+
+
+def hurst(X):
+    """ Compute the Hurst exponent of X. If the output H=0.5,the behavior
+    of the time-series is similar to random walk. If H<0.5, the time-series
+    cover less "distance" than a random walk, vice verse.
+
+    Parameters
+    ----------
+
+    X
+
+        list
+
+        a time series
+
+    Returns
+    -------
+    H
+
+        float
+
+        Hurst exponent
+
+    Notes
+    --------
+    Author of this function is Xin Liu
+
+    Examples
+    --------
+
+    >>> import pyeeg
+    >>> from numpy.random import randn
+    >>> a = randn(4096)
+    >>> pyeeg.hurst(a)
+    0.5057444
+
+    """
+    X = numpy.array(X)
+    N = X.size
+    T = numpy.arange(1, N + 1)
+    Y = numpy.cumsum(X)
+    Ave_T = Y / T
+
+    S_T = numpy.zeros(N)
+    R_T = numpy.zeros(N)
+
+    for i in range(N):
+        S_T[i] = numpy.std(X[:i + 1])
+        X_T = Y - T * Ave_T[i]
+        R_T[i] = numpy.ptp(X_T[:i + 1])
+
+    R_S = R_T / S_T
+    R_S = numpy.log(R_S)[1:]
+    n = numpy.log(T)[1:]
+    A = numpy.column_stack((n, numpy.ones(n.size)))
+    [m, c] = numpy.linalg.lstsq(A, R_S)[0]
+    H = m
+    return H