Efficient and easy to use fractional differentiation transformations for stationarizing time series data in Python.
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.
pip install tsfracdiff
# Required
python3 # Python 3.7+
numpy
pandas
arch
# Suggested
joblib
# 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)For a more in-depth example, see this notebook.
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.