tsuniverse
A library for efficiently processing a time series universe to determine causal features.
Dependencies :globe_with_meridians:
Python 3.11.6:
Raison D'être :thought_balloon:
tsuniverse aims take a universe of time series and figure out features from that universe that can be used to predict a single time series.
Architecture :triangular_ruler:
tsuniverse is a functional library, meaning that each phase of the feature extraction goes through functions without side-effects. It attempts to do as much multiprocessing as it can to make this process quicker. Each feature extraction is done in different phases, those phases are:
- Pearson Correlations.
- Mutual Information.
- Spearmans Rho.
- Kendalls Tau.
- Distance Correlations.
- HSIC.
- Stumpy.
Installation :inbox_tray:
This is a python package hosted on pypi, so to install simply run the following command:
pip install tsuniverse
or install using this local repository:
python setup.py install --old-and-unmanageable
Usage example :eyes:
The use of tsuniverse is entirely through code due to it being a library. It attempts to hide most of its complexity from the user, so it only has a few functions of relevance in its outward API.
Generating Features
To generate features:
import datetime
import pandas as pd
from tsuniverse.process import process
df = ... # Your timeseries dataframe
features = process(df)
This will produce a list of features that you can produce with timeseries-features.
License :memo:
The project is available under the MIT License.
1 maintainer
Metadata
1 maintainer
Release files for tsuniverse 0.0.12
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