adamvvu/tsfracdiff
Efficient and easy to use fractional differentiation transformations for stationarizing time series data in Python.
This tool helps quantitative analysts and data scientists prepare time series data, like financial market prices, for predictive models. It takes in raw, non-stationary time series data and outputs a transformed version that is more stable and suitable for machine learning or statistical analysis, while retaining crucial information often lost with traditional methods. Traders, researchers, and anyone building models on time-dependent data will find this useful.
No commits in the last 6 months. Available on PyPI.
Use this if you need to transform volatile time series data, especially in finance, to improve the performance of your predictive models without sacrificing valuable information.
Not ideal if you are working with simple, stationary time series where basic differencing or no transformation is already sufficient.
Stars
22
Forks
4
Language
Python
License
MIT
Category
Last pushed
Feb 10, 2023
Commits (30d)
0
Dependencies
4
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