Výsledky vyhledávání - "Truong, Charles"

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ASTRIDE: Adaptive Symbolization for Time Series Databases

Autor: Combettes, Sylvain W., Truong, Charles, Oudre, Laurent

We introduce ASTRIDE (Adaptive Symbolization for Time seRIes DatabasEs), a novel symbolic representation of time series, along with its accelerated variant FASTRIDE (Fast ASTRIDE). Unlike most symbolization procedures, ASTRIDE is adaptive during both

Externí odkaz: http://arxiv.org/abs/2302.04097

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Meta-analysis parameters computation: a Python approach to facilitate the crossing of experimental conditions

Autor: Quijoux, Flavien, Truong, Charles, Vienne-Jumeau, Aliénor, Oudre, Laurent, BERTIN-HUGAULT, François, ZAWIEJA, Philippe, LEFEVRE, Marie, VIDAL, Pierre-Paul, RICARD, Damien

Meta-analysis is a data aggregation method that establishes an overall and objective level of evidence based on the results of several studies. It is necessary to maintain a high level of homogeneity in the aggregation of data collected from a system

Externí odkaz: http://arxiv.org/abs/2007.07799

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Dissertation/ Thesis

Détection de ruptures multiples – application aux signaux physiologiques.

Autor: Truong, Charles

Ce travail s’intéresse au problème de détection de ruptures multiples dans des signaux physiologiques (univariés ou multivariés). Ce type de signaux comprend par exemple les électrocardiogrammes (ECG), électroencéphalogrammes (EEG), les mes

Externí odkaz: http://www.theses.fr/2018SACLN030/document

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ruptures: change point detection in Python

Autor: Truong, Charles, Oudre, Laurent, Vayatis, Nicolas

ruptures is a Python library for offline change point detection. This package provides methods for the analysis and segmentation of non-stationary signals. Implemented algorithms include exact and approximate detection for various parametric and non-

Externí odkaz: http://arxiv.org/abs/1801.00826

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Selective review of offline change point detection methods

Autor: Truong, Charles, Oudre, Laurent, Vayatis, Nicolas

Publikováno v: Signal Processing, 167:107299, 2020

This article presents a selective survey of algorithms for the offline detection of multiple change points in multivariate time series. A general yet structuring methodological strategy is adopted to organize this vast body of work. More precisely, d

Externí odkaz: http://arxiv.org/abs/1801.00718

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