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+34 946 567 842
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+34 946 567 842
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umori@bcamath.org
Information of interest
- Orcid: 0000-0002-2057-1770
Usue Mori received the MSc degree in mathematics, and a PhD in computer science from the University of the Basque Country UPV/EHU, Spain, in 2010 and 2015, respectively. She is currently working as a lecturer in the Department of Computer Science and Artificial Intelligence of the University of the Basque Country UPV/EHU.
Her research focuses on time series mining. Particularly, the main goal of her research is to provide methodological solutions to problems in the area of time series classification, anomaly detection in temporal data, clustering time series, etc. In addition she also works actively in applications in Industry 4.0, cibersecurity and health collaborating with different companies and technological centers.
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Time Series Classifier Recommendation by a Meta-Learning Approach
Abanda, A.
; Mori, U.; Lozano, J.A. (2022-03-26)
This work addresses time series classifier recommendation for the first time in the literature by considering several recommendation forms or meta-targets: classifier accuracies, complete ranking, top-M ranking, best set ...
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Ad-Hoc Explanation for Time Series Classification
Abanda, A.; Mori, U.; Lozano, J.A. (2022)
In this work, a perturbation-based model-agnostic explanation method for time series classification is presented. One of the main novelties of the proposed method is that the considered perturbations are interpretable and ...
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A Multivariate Time Series Streaming Classifier for Predicting Hard Drive Failures [Application Notes]
Ircio, J.; Lojo, A.; Mori, U.; Lozano, J.A. (2022)
Digital data storage systems such as hard drives can suffer breakdowns that cause the loss of stored data. Due to the cost of data and the damage that its loss entails, hard drive failure prediction is vital. In this ...
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A Review on Outlier/Anomaly Detection in Time Series Data
Blázquez-García, A.; Conde, A.; Mori, U.; Lozano, J.A. (2021)
Recent advances in technology have brought major breakthroughs in data collection, enabling a large amount of data to be gathered over time and thus generating time series. Mining this data has become an important task for ...
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Water leak detection using self-supervised time series classification
Blázquez-García, A.; Conde, A.; Mori, U.; Lozano, J.A. (2021)
Leaks in water distribution networks cause a loss of water that needs to be com- pensated to ensure a continuous supply for all customers. This compensation is achieved by increasing the flow of the network, which entails ...
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Mutual information based feature subset selection in multivariate time series classification
Ircio, J.; Lojo, A.; Mori, U.; Lozano, J.A. (2020)
This paper deals with supervised classification of multivariate time se- ries. In particular, the goal is to propose a filter method to select a subset of time series. Consequently, we adopt the framework proposed by Brown ...
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Early classification of time series using multi-objective optimization techniques
Mori, U.; Mendiburu, A.; Miranda, I.M.; Lozano, J.A. (2019-04-23)
In early classification of time series the objective is to build models which are able to make class-predictions for time series as accurately and as early as possible, when only a part of the series is available. It is ...
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A review on distance based time series classification
Abanda, A.; Mori, U.; Lozano, J.A. (2018-11-01)
Time series classification is an increasing research topic due to the vast amount of time series data that is being created over a wide variety of fields. The particularity of the data makes it a challenging task and ...
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Early classification of time series by simultaneously optimizing the accuracy and earliness
Mori, U.; Mendiburu, A.; Dasgupta, S.; Lozano, J.A. (2017-10)
The problem of early classi cation of time series appears naturally in contexts where the data, of temporal nature, is collected over time, and early class predictions are interesting or even required. The objective is to ...