Izaskun Oregui

PhD candidate

Izaskun Oregi received the degree in Physichs from the University of the Basque Country, Spain, in 2011. In 2012 and 2016 she got her M.Sc. degrees in Physics of Complex Networks from the Politechnical University of Madrid and in Mathematical Engineering from the Universidad Complutense of Madrid, respectively. She is currently a PhD student at TECNALIA. Her main areas of research interest are on-line and off-line time series data mining, supervised/unsupervised classification and adversarial machine learning.

CONTACT INFORMATION:

  • +34 664 119 377
  • izaskun.oregui@tecnalia.com

PUBLICATIONS

  • I. Laña, J. Del Ser, M. Vélez, and I. Oregi, “Joint feature selection and parameter tuning for short-term traffic flow forecasting based on heuristically optimized multi-layer neural networks,” in International conference on harmony search algorithm, 2017, p. 91–100.
    [Bibtex] 
    @inproceedings{lana2017joint,
title={Joint feature selection and parameter tuning for short-term traffic flow forecasting based on heuristically optimized multi-layer neural networks},
author={La{\~n}a, Ibai and Del Ser, Javier and V{\'e}lez, Manuel and Oregi, Izaskun},
booktitle={International Conference on Harmony Search Algorithm},
pages={91--100},
year={2017},
organization={Springer, Singapore}
}
  • E. Villar-Rodriguez, J. Del Ser, I. Oregi, M. N. Bilbao, and S. Gil-Lopez, “Detection of non-technical losses in smart meter data based on load curve profiling and time series analysis,” Energy, vol. 137, p. 118–128, 2017.
    [Bibtex] 
    @article{villar2017detection,
title={Detection of non-technical losses in smart meter data based on load curve profiling and time series analysis},
author={Villar-Rodriguez, Esther and Del Ser, Javier and Oregi, Izaskun and Bilbao, Miren Nekane and Gil-Lopez, Sergio},
journal={Energy},
volume={137},
pages={118--128},
year={2017},
publisher={Pergamon}
}
  • I. Oregi, J. Del Ser, A. Pérez, and J. A. Lozano, “Nature-inspired approaches for distance metric learning in multivariate time series classification,” in Evolutionary computation (cec), 2017 ieee congress on, 2017, p. 1992–1998.
    [Bibtex] 
    @inproceedings{oregi2017nature,
title={Nature-inspired approaches for distance metric learning in multivariate time series classification},
author={Oregi, Izaskun and Del Ser, Javier and P{\'e}rez, Aritz and Lozano, Jos{\'e} A},
booktitle={Evolutionary Computation (CEC), 2017 IEEE Congress on},
pages={1992--1998},
year={2017},
organization={IEEE}
}
  • I. Oregi, A. Pérez, J. Del Ser, and J. A. Lozano, “On-line dynamic time warping for streaming time series,” in Joint european conference on machine learning and knowledge discovery in databases, 2017, p. 591–605.
    [Bibtex] 
    @inproceedings{oregi2017line,
title={On-Line Dynamic Time Warping for Streaming Time Series},
author={Oregi, Izaskun and P{\'e}rez, Aritz and Del Ser, Javier and Lozano, Jos{\'e} A},
booktitle={Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
pages={591--605},
year={2017},
organization={Springer, Cham}
}
  • I. Oregi, J. Del Ser, A. Perez, and J. A. Lozano, “Adversarial sample crafting for time series classification with elastic similarity measures,” in International symposium on intelligent and distributed computing, 2018, p. 26–39.
    [Bibtex] 
    @inproceedings{oregi2018adversarial,
title={Adversarial Sample Crafting for Time Series Classification with Elastic Similarity Measures},
author={Oregi, Izaskun and Del Ser, Javier and Perez, Aritz and Lozano, Jose A},
booktitle={International Symposium on Intelligent and Distributed Computing},
pages={26--39},
year={2018},
organization={Springer, Cham}
}