Interpretable Feature Construction for Time Series Extrinsic Regression

Dominique Gay, Alexis Bondu, Vincent Lemaire 0001, Marc Boullé. Interpretable Feature Construction for Time Series Extrinsic Regression. In Kamal Karlapalem, Hong Cheng 001, Naren Ramakrishnan, R. K. Agrawal, P. Krishna Reddy, Jaideep Srivastava, Tanmoy Chakraborty, editors, Advances in Knowledge Discovery and Data Mining - 25th Pacific-Asia Conference, PAKDD 2021, Virtual Event, May 11-14, 2021, Proceedings, Part I. Volume 12712 of Lecture Notes in Computer Science, pages 804-816, Springer, 2021. [doi]

@inproceedings{GayB0B21,
  title = {Interpretable Feature Construction for Time Series Extrinsic Regression},
  author = {Dominique Gay and Alexis Bondu and Vincent Lemaire 0001 and Marc Boullé},
  year = {2021},
  doi = {10.1007/978-3-030-75762-5_63},
  url = {https://doi.org/10.1007/978-3-030-75762-5_63},
  researchr = {https://researchr.org/publication/GayB0B21},
  cites = {0},
  citedby = {0},
  pages = {804-816},
  booktitle = {Advances in Knowledge Discovery and Data Mining - 25th Pacific-Asia Conference, PAKDD 2021, Virtual Event, May 11-14, 2021, Proceedings, Part I},
  editor = {Kamal Karlapalem and Hong Cheng 001 and Naren Ramakrishnan and R. K. Agrawal and P. Krishna Reddy and Jaideep Srivastava and Tanmoy Chakraborty},
  volume = {12712},
  series = {Lecture Notes in Computer Science},
  publisher = {Springer},
  isbn = {978-3-030-75762-5},
}