tsfracdiff 1.0.0

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Efficient and easy to use fractional differentiation transformations for

stationarizing time series data in Python.

tsfracdiff

Data with high persistence, serial correlation, and non-stationarity

pose significant challenges when used directly as predictive signals in

many machine learning and statistical models. A common approach is to

take the first difference as a stationarity transformation, but this

wipes out much of the information available in the data. For datasets

where there is a low signal-to-noise ratio such as financial market

data, this effect can be particularly severe. Hosking (1981) introduces

fractional (non-integer) differentiation for its flexibility in modeling

short-term and long-term time series dynamics, and López de Prado (2018)

proposes the use of fractional differentiation as a feature

transformation for financial machine learning applications. This library

is an extension of their ideas, with some modifications for efficiency

and robustness.

Getting Started

Installation

pip install tsfracdiff

Dependencies:

# Required

python3 # Python 3.6+

numpy

pandas

arch    # If on Python 3.6, use arch <= v4.17



# Suggested

joblib

Usage

# A pandas.DataFrame/np.array with potentially non-stationary time series

df 



# Automatic stationary transformation with minimal information loss

from tsfracdiff import FractionalDifferentiator

fracDiff = FractionalDifferentiator()

df = fracDiff.FitTransform(df)

Documentation/Examples

For a more in-depth example, see the notebook in /examples. See

/docs for documentation.

References

Hosking, J. R. M. (1981). Fractional Differencing. Biometrika, 68(1),

165--176. https://doi.org/10.2307/2335817

López de Prado, Marcos (2018). Advances in Financial Machine Learning.

John Wiley & Sons, Inc.