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dc.contributor Graduate Program in Computational Science and Engineering.
dc.contributor.advisor Cemgil, Ali Taylan.
dc.contributor.author Poyraz, Onur.
dc.date.accessioned 2023-03-16T10:04:17Z
dc.date.available 2023-03-16T10:04:17Z
dc.date.issued 2019.
dc.identifier.other CSE 2019 P78
dc.identifier.uri http://digitalarchive.boun.edu.tr/handle/123456789/12403
dc.description.abstract Anomaly detection (AD) is the discovery of the observations which does not con form with the rest of the observations. The types of anomalies and their occurrences that exist in the data set are tried to be determined. On the other hand, time series structures have dynamic structures, which are evolving over time, and in such struc tures, observations will be affected by previous observations. This thesis focuses on the anomaly detection process under time series structures. This problem is not always straightforward because the definition of anomaly could change with the context of the dynamic structure and anomaly detection process in the system could interfere with the intense noises at the observations. In this thesis, we try to identify anomalies in the sub-sequences of the stream ing data. When doing so, we also want to discriminate the anomalies in the system with the faulty observations. Therefore we investigate collective anomalies in the data. We propose both statistical inference methods and deep learning approaches for such type of anomaly detection in time series (ADTS) problem. We use a Gaussian mix ture model (GMM) and a customized hidden Markov model (HMM) as statistical approaches, while we use Recurrent Neural Networks (RNN) and Long Short-Term Memories (LSTM) as deep learning approaches. Except for GMMs, we take into ac count the sequential structures of data sets in the models proposed above. We apply our methodologies to the Borusan wind turbines data and we compare the model results with the experiments we performed on this dataset.
dc.format.extent 30 cm.
dc.publisher Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2019.
dc.subject.lcsh Time series analysis.
dc.title Anomaly detection in time series
dc.format.pages xii, 66 leaves ;


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