Co-eye: a multi-resolution ensemble classifier for symbolically approximated time series

This is an implementation of Co-eye Algorithm for Time Series Classification. You can access the paper from https://link.springer.com/article/10.1007/s10994-020-05887-3

Description

• UCR folder: contains datasets, imported from UCRarchive https://www.cs.ucr.edu/~eamonn/time_series_data_2018/
• Co-eye.ipynb: implementation of Co-eye algorithm in Jupyter notebook with output displayed.

Citation

if you use co-eye in a scientific publication, we would appreciate citations to the following paper:

Abdallah, Z.S., Gaber, M.M. Co-eye: a multi-resolution ensemble classifier for symbolically approximated time series. Mach Learn 109, 2029–2061 (2020). https://doi.org/10.1007/s10994-020-05887-3

Algorithm overview

Requirements:

• Python 3.6
• pyts https://pyts.readthedocs.io/en/stable/
• imblearn https://pypi.org/project/imblearn/