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Faculté des Sciences appliquées
Faculté des Sciences appliquées
MASTER THESIS
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Master's Thesis : Time series analysis with machine learning

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Crasset, Tom ULiège
Promotor(s) : Geurts, Pierre ULiège
Date of defense : 25-Jun-2020/26-Jun-2020 • Permalink : http://hdl.handle.net/2268.2/9040
Details
Title : Master's Thesis : Time series analysis with machine learning
Translated title : [fr] Analyse de séries temporelles avec de l'apprentissage machine
Author : Crasset, Tom ULiège
Date of defense  : 25-Jun-2020/26-Jun-2020
Advisor(s) : Geurts, Pierre ULiège
Committee's member(s) : Wehenkel, Louis ULiège
Louppe, Gilles ULiège
Borlée, Benoit 
Language : English
Number of pages : 112
Discipline(s) : Engineering, computing & technology > Computer science
Target public : Researchers
Professionals of domain
Institution(s) : Université de Liège, Liège, Belgique
Degree: Master : ingénieur civil en science des données, à finalité spécialisée
Faculty: Master thesis of the Faculté des Sciences appliquées

Abstract

[en] Many industrial companies have production chains that run in batches, as opposed to
a continuous production chain. The monitoring of their production chains is crucial
and yields time series data. PEPITe, a data analytics company, works which one such
industrial company in particular whose yield in one batch production chain fluctuates
without apparent reason. So far, PEPITe’s arsenal of traditional machine learning and
statistical methods has not provided sufficient results. This work’s objective consists
of two parts. The first part aims at finding unsupervised algorithms on time series
data that PEPITe could add to their tool box. The second part is more specific to the
problem of the industrial company, which aims at finding a supervised method capable
of providing interpretable results.
Regarding the first objective, 4 algorithms were selected from the literature, implemented and showcased on the provided industrial data set. Regarding the second
objective, two interpretable methods, namely a convolutional neural network (CNN)
and a classifier based on shapelets, were tested on the provided data set and compared
against 2 baselines.
The implementation of the unsupervised algorithms features code with permissive
licenses to be able to build upon this work. The performance of all the supervised models was lower than expected, however the CNN allowed for a kind of interpretability
with a comparable performance to the baselines.


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Author

  • Crasset, Tom ULiège Université de Liège > Master ingé. civ. sc. don. à . fin.

Promotor(s)

Committee's member(s)

  • Wehenkel, Louis ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Méthodes stochastiques
    ORBi View his publications on ORBi
  • Louppe, Gilles ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Big Data
    ORBi View his publications on ORBi
  • Borlée, Benoit
  • Total number of views 97
  • Total number of downloads 4










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