Interpretable time series classification using linear models and multi-resolution multi-domain symbolic representations

@article{Nguyen2019InterpretableTS,
  title={Interpretable time series classification using linear models and multi-resolution multi-domain symbolic representations},
  author={Thach Le Nguyen and Severin Gsponer and Iulia Ilie and Martin O'Reilly and Georgiana Ifrim},
  journal={Data Mining and Knowledge Discovery},
  year={2019},
  volume={33},
  pages={1183-1222}
}
The time series classification literature has expanded rapidly over the last decade, with many new classification approaches published each year. Prior research has mostly focused on improving the accuracy and efficiency of classifiers, with interpretability being somewhat neglected. This aspect of classifiers has become critical for many application domains and the introduction of the EU GDPR legislation in 2018 is likely to further emphasize the importance of interpretable learning algorithms… 

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