_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
 / / /      / / /___/ / /     \ \ \ /_/\__/ / /
/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/
python-tsfresh 0.21.1
Propagated dependencies: python-cloudpickle@3.1.0 python-dask@2024.12.1 python-distributed@2024.12.1 python-numpy@1.26.4 python-pandas@2.2.3 python-patsy@1.0.1 python-pywavelets@1.8.0 python-requests@2.32.5 python-scikit-learn@1.7.0 python-scipy@1.12.0 python-statsmodels@0.14.4 python-stumpy@1.13.0 python-tqdm@4.67.1
Channel: guix-science
Location: guix-science/packages/time-series.scm (guix-science packages time-series)
Home page: https://tsfresh.readthedocs.io/
Licenses: Expat
Synopsis: Automatic extraction of relevant features from time series
Description:

The package provides systematic time-series feature extraction by combining established algorithms from statistics, time-series analysis, signal processing, and nonlinear dynamics with a robust feature selection algorithm. In this context, the term time-series is interpreted in the broadest possible sense, such that any types of sampled data or even event sequences can be characterised.

Total results: 1