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Laboratoire d'InfoRmatique en Image et Systèmes d'information

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Laboratoire d'InfoRmatique en Image et Systèmes d'information
UMR 5205 CNRS / INSA Lyon / Université Claude Bernard Lyon 1 / Université Lumière Lyon 2 / École Centrale de Lyon
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Seif-Eddine Benkabou

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PhD student

Team Data Mining and Machine Learning
 
Institution Claude Bernard University of Lyon 1
Location Nautibus (Université Lyon1)
 
E-mail seif-eddine.benkabou at liris.cnrs.fr
Contact details Publications Thesis
 
Subject Outlier detection from time series data: Application to tyre data
Abstract Anomaly detection is a crucial task that has attracted the interest of several research studies in machine learning and data mining communities. The complexity of this task depends on the nature of the data, the availability of their labeling and the application framework on which they depend. As part of this thesis, we address this problem for complex data and particularly for uni and multivariate time series. The term "anomaly" can refer to an observation that deviates from other observations so as to arouse suspicion that it was generated by a different generation process. More generally, the underlying problem (also called novelty detection or outlier detection) aims to identify, in a set of data, those which differ significantly from others, which do not conform to an "expected behavior" (which could be defined or learned), and which indicate a different mechanism. The "abnormal" patterns thus detected often result in critical information. We focus specifically on two particular aspects of anomaly detection from time series in an unsupervised fashion. The first is global and consists in detecting abnormal time series compared to an entire database, whereas the second one is called contextual and aims to detect locally, the abnormal points with respect to the global structure of the relevant time series.
Advisor Khalid Benabdeslem

Last update : 2018-02-01 15:30:28