Computed HCTSA matrices for the UEA/UCR 2018 time-series classification tasks
Using the hctsa toolbox v0.97 (link in References below), we computed 7,500+ time-series features on each of the time-series classification tasks contained in the UEA/UCR Time Series Classification Repository. This repository provides the computed hctsa output files (.mat-files) for each classification task.
We used the computed feature matrices to select a small subset of 22 hctsa estimators (termed catch22) that were the most useful for the UEA/UCR datasets:
C.H. Lubba, S.S. Sethi, P. Knaute, S.R. Schultz, B.D. Fulcher, N.S. Jones. catch22: CAnonical Time-series CHaracteristics. arXiv (2019). https://arxiv.org/abs/1901.10200
The matrices can be read in from Python as well using the Matlab_IO interface for which examples can be found in our selection pipeline for catch22 ("op_importance" in References) and in the "hctsaAnalysisPython" GitHub repository.
