HT

class fathon.HT(tsVec)

Bases: object

Time-dependent local Hurst exponent class.

Parameters:
tsVec : iterable

Time series used for the analysis.

ht : numpy ndarray

Time-dependent local Hurst exponent.

computeHt(scales, polOrd=1, mfdfaPolOrd=1, q0Fit=[], verbose=False)

Computation of the time-dependent local Hurst exponent at each scale, using Ihlen’s approach.

Parameters:
scales : int or iterable or numpy ndarray

Window’s sizes used for the computation of the time-dependent local Hurst exponent.

polOrd : int, optional

Order of the polynomial to be fitted in each window (default : 1).

mfdfaPolOrd : int, optional

Order of the polynomial to be fitted to MFDFA’s fluctuations at q = 0 (default : 1).

q0Fit : iterable or numpy ndarray of floats, optional

MFDFA’s Hurst exponent at order q = 0 and the corresponding intercept of the fit, [hq0, hq0_intercept]. These values must come from a log-log fit, with the log base equal to e. If not empty, it will be directly used to compute the time-dependent local Hurst exponent, ignoring mfdfaPolOrd value (default : []).

verbose : bool, optional

Verbosity (default : False).

Returns:
numpy ndarray

Time-dependent local Hurst exponent.

saveObject(outFileName)

Save current object state to binary file.

Parameters:
outFileName : str

Output binary file. .fathon extension will be appended to the file name.

Usage examples

import numpy as np
import fathon
from fathon import fathonUtils as fu

#time series
a = np.random.randn(10000)

#zero-mean cumulative sum
a = fu.toAggregated(a)

#initialize ht object
pyht = fathon.HT(a)
#compute time-dependent Hurst exponent
ht = pyht.computeHt([100, 200, 1000], mfdfaPolOrd=1, polOrd=1)