Computed HCTSA matrices for the UEA/UCR 2018 time-series classification tasks
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https://figshare.com/articles/Computed_HCTSA_matrices_for_the_UEA_UCR_2018_time-series_classification_tasks/6865163/1
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资源简介：
Using the <i>hctsa</i> 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 <i>hctsa</i> output files (.mat-files) for each classification task.<br>We used the computed feature matrices to select a small subset of 22 <i>hctsa</i> estimators (termed <i>catch22</i>) 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. <i>catch22</i>: CAnonical Time-series CHaracteristics. arXiv (2019). https://arxiv.org/abs/1901.10200<br><br>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 <i>catch22</i> ("op_importance" in References) and in the "hctsaAnalysisPython" GitHub repository.
提供机构：
figshare
创建时间：
2019-02-17
