Amaia Abanda received her degree in Mathematics at the Autonomous University of Barcelona in 2013. In 2015 se obtained her MSc in Computational Engineering and Intelligent Systems at the University of the Basque Country.
During 2016, she worked as a research technician at the Computer Vision in Pattern Recognition and Applications Lab, University of Cagliari (Italy). She joined Basque Center for Applied Mathematics (BCAM) in 2017, as a PhD student in Machine Learning Group.
Her doctoral thesis, Contributions to time series classification: meta-learning and explainability, has been supervised by Jose Antonio Lozano (BCAM-UPV/EHU) and Usue Mori (UPV/EHU).
The defense will take place in the lecture hall of the Faculty of Science at UPV/EHU, and will be streaming through the platform Webex. It will take place on Friday, January 14 at 10:45, and users will be able to follow it live using the following link: https://ehu.webex.com/ehu/j.php?MTID=m2f805fedc78f506ea61915cc68d147c8
On behalf of all BCAM members, we would like to wish Amaia the best of luck in his upcoming thesis defense.
PhD thesis Title:
Contributions to time series classification: meta-learning and explainability.
This thesis includes 3 contributions of different types to the area of supervised time series classification, a growing field of research due to the amount of time series collected daily in a wide variety of domains. In this context, the number of methods available for classifying time series is increasing, and the classifiers are becoming more and more competitive and varied. Thus, the first contribution of the thesis consists of proposing a taxonomy of distance-based time series classifiers, where an exhaustive review of the existing methods and their computational costs is made. Moreover, from the point of view of a non-expert user (even from that of an expert), choosing a suitable classifier for a given problem is a difficult task. The second contribution, therefore, deals with the recommendation of time series classifiers, for which we will use a meta-learning approach. Finally, the third contribution consists of proposing a method to explain the prediction of time series classifiers, in which we calculate the relevance of each region of a series in the prediction. This method of explanation is based on perturbations, for which we will consider specific and realistic transformations for the time series.