Efficient and easy to use fractional differentiation transformations for stationarizing time series data.
Project description
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 3.7+
numpy
pandas
arch
# 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)
For a more in-depth example, see this notebook.
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.
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