This repository provides a random forest classifier, catch22Forest, for time-series based on catch22 features, a collection of 22 time-series features selected by their classification performance from a much larger set of 7500+ features of the hctsa toolbox. Features are implemented in C and wrapped for Python. catch22 is distributed under the GNU General Public License Version 3.
catch22Forest requires
- Python (>= 3.4)
- NumPy (>= 1.8.2)
- sktime (>= 0.3.0)
- catch22 (>=0.0.1)
Using setuptools
python setup.py install
To install the requirements, use:
pip install -r requirements.txt
See the examples folder, containing two examples:
For information on how this feature set was constructed see the paper:
- C.H. Lubba, S.S. Sethi, P. Knaute, S.R. Schultz, B.D. Fulcher, N.S. Jones. catch22: CAnonical Time-series CHaracteristics. Data Mining and Knowledge Discovery 33, 6 (2019).
For information on the full set of over 7000 features, see the following (open) publications:
- B.D. Fulcher and N.S. Jones. hctsa: A computational framework for automated time-series phenotyping using massive feature extraction. Cell Systems 5, 527 (2017).
- B.D. Fulcher, M.A. Little, N.S. Jones Highly comparative time-series analysis: the empirical structure of time series and their methods. J. Roy. Soc. Interface 10, 83 (2013).