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Anomaly Detection for time series using Shapelets
Disponibile dal
11/03/2022
Presso
ADDFOR-Industriale
Altre informazioni

Time series classification is the task of assigning a label to the given time series. This has applications in several fields, such as finance, medicine and industry. To solve this task, in the literature, Shapelets have been proposed [1]. Shapelets are subsequences of a time series that capture the characteristics of a certain class of time series in a dataset. Using these Shapelets, it is possible to cluster the time series in the dataset, by assigning a time series the label of a class based on the presence or absence of the candidate Shapelet representative of that class [2]. Shapelets can be used in the context of unsupervised anomaly detection [3], allowing to identify the anomalous time series based on the absence of normality characteristics learned by the model through the candidateShaplets ofthe normal class.

The goal of this thesis is to study the recent advances in the classification of time series using Shapelets, with particular focus on unsupervised models oriented towards anomaly detection and to apply these algorithms to a real industrial problem.

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Who we are looking for

Students that are about to get their Master Degree in: computer science, computer engineering, mechatronic engineering, mathemati cal engineering, mathematics, physics, physics of complex systems, informatics.

Required skills are:

  • Proficiency in at least one programming language (Python,

    C++, Matlab, Java), Python is preferred;

  • Basic knowledge of machine learning and Deep

    Learning algorithms. The candidate will acquire:

  • Expertise on modern Deep Learning;

  • Expertise in time series classification and anomaly detection;

  • Experience in programming with Python.

Planned activities

The planned activities for this thesis are:

  • Initial research on time series classification and Shapelets;

  • Initial research on time series anomaly detection using

    Shapelets;

  • Implementation of state-of-the-art methods;

  • Application of these methods to a real industrial problem.

How to contact us

Directly by email to: R.Tatano@vargroup.it
By LinkedIn: https://www.linkedin.com/in/rosalia-tatano-19a87770/

 

Check these links before moving on

[1]J.Zakaria,A.Mueen andE.Keogh,"ClusteringTimeSeriesUsingUnsupervised-Shapelets,"2012IEEE12thInternational Conference on Data Mining, 2012, pp. 785-794, doi: 10.1109/ICDM.2012.26.

[2]QinZhang,JiaWu,HongYang,YingjieTian,andChengqi Zhang.2016.Unsupervisedfeaturelearningfromtimeseries.In Proceedings of the Twenty-Fifth International JointConference on ArtificialIntelligence(IJCAI'16).AAAI Press,2322–2328.
[3] Beggel, L., Kausler, B.X., Schiegg, M. et al. Time series anomaly detection based on shapelet learning. Comput Stat 34, 945 – 976 (2019). https://doi.org/10.1007/s00180-018-0824-9

 

Stato
Disponibile

Rivolgersi a:

Docente
Filippo De Lillo
Email
filippo.delillo@unito.it
Telefono
0116707428
Ultimo aggiornamento: 11/03/2022 21:47
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