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Classification ensembliste des séries temporelles multivariées basée sur les M-histogrammes et une approche multi-vues

Abstract : Recording measurements about various phenomena and exchanging information about it, participate in the emergence of a type of data called time series. Today humongous quantities of those data are often collected. A time series is characterized by numerous points and interactions can be observed between those points. A time series is multivariate when multiple measures are recorded at each timestamp, meaning a point is, in fact, a vector of values. Even if univariate time series, one value at each timestamp, are well-studied and defined, it’s not the case of multivariate one, for which the analysis is still challenging. Indeed, it is not possible to apply directly techniques of classification developed on univariate data to the case of multivariate one. In fact, for this latter, we have to take into consideration the interactions not only between points but also between dimensions. Moreover, in industrial cases, as in Michelin company, the data are big and also of different length in terms of points size composing the series. And this brings a new complexity to deal with during the analysis. None of the current techniques of classifying multivariate time series satisfies the following criteria, which are a low complexity of computation, dealing with variation in the number of points and good classification results. In our approach, we explored a new tool, which has not been applied before for MTS classification, which is called M-histogram. A M-histogram is a visualization tool using M axis to project the density function underlying the data. We have employed it here to produce a new representation of the data, that allows us to bring out the interactions between dimensions. Searching for links between dimensions correspond particularly to a part of learning techniques called multi-view learning. A view is an extraction of dimensions of a dataset, which are of same nature or type. Then the goal is to display the links between the dimensions inside each view in order to classify all the data, using an ensemble classifier. So we propose a multi-view ensemble model to classify multivariate time series. The model creates multiple M-histograms from differents groups of dimensions. Then each view allows us to get a prediction which we can aggregate to get a final prediction. In this thesis, we show that the proposed model allows a fast classification of multivariate time series of different sizes. In particular, we applied it on aMichelin use case.
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Angéline Plaud. Classification ensembliste des séries temporelles multivariées basée sur les M-histogrammes et une approche multi-vues. Modélisation et simulation. Université Clermont Auvergne, 2019. Français. ⟨NNT : 2019CLFAC047⟩. ⟨tel-02502618⟩

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