indexes.py 文件源码

def hurst(ts):
    # Create the range of lag values
    window_len = 30
    hurst_ts = np.zeros((0,), dtype=np.int)
    for tail in range(window_len, len(ts)):
        lags = range(1, window_len // 2)
        # print(lags)
        # Calculate the array of the variances of the lagged differences
        cur_ts = ts[max(1, tail - window_len):tail]
        # cur_ts = ts[:tail]
        tau = [sqrt(std(subtract(cur_ts[lag:], cur_ts[:-lag]))) for lag in lags]

        # print("Time series slice len: ",len(cur_ts),len(tau),lags)
        # Use a linear fit to estimate the Hurst Exponent
        poly = polyfit(log(lags), log(tau), 1)

        # Return the Hurst exponent from the polyfit output
        # print(poly[0]*2.0)
        hurst_ts = np.append(hurst_ts, poly[0] * 2.0)
    return hurst_ts