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Allou Samé
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2020 – today
- 2024
- [j18]Paul de Nailly
, Etienne Côme, Latifa Oukhellou, Allou Samé, Jacques Ferriere, Yasmine Merad-Boudia:
Multivariate count time series segmentation with "sums and shares" and Poisson lognormal mixture models: a comparative study using pedestrian flows within a multimodal transport hub. Adv. Data Anal. Classif. 18(2): 455-491 (2024) - [j17]Paul de Nailly, Etienne Côme, Latifa Oukhellou, Allou Samé, Jacques Ferriere, Yasmine Merad-Boudia:
Deep Probabilistic Forecasting of Multivariate Count Data With "Sums and Shares" Distributions: A Case Study on Pedestrian Counts in a Multimodal Transport Hub. IEEE Trans. Intell. Transp. Syst. 25(11): 15687-15701 (2024) - [i9]Hugues Moreau, Etienne Côme, Allou Samé, Latifa Oukhellou:
A Regression Mixture Model to understand the effect of the Covid-19 pandemic on Public Transport Ridership. CoRR abs/2402.12392 (2024) - 2023
- [c29]Hugues Moreau
A Regression Mixture Model to understand the effect of the Covid-19 pandemic on Public Transport Ridership. ICDM (Workshops) 2023: 1259-1268 - [c28]Allou Samé:
Detection of Exponential Regimes from Time Series for Characterizing the Thermal Dynamics of Buildings. ICMLA 2023: 1669-1675 - 2022
- [j16]Louise Bonfils, Allou Samé, Latifa Oukhellou
Dynamic clustering and modeling of temporal data subject to common regressive effects. Neurocomputing 500: 217-230 (2022) - [j15]Kevin Pasini, Mostepha Khouadjia, Allou Samé, Martin Trépanier
Contextual anomaly detection on time series: a case study of metro ridership analysis. Neural Comput. Appl. 34(2): 1483-1507 (2022) - [c27]Matthias Heinrich, Marie Ruellan, Jean-Pierre Lévy, Allou Samé, Latifa Oukhellou:
What insights can we draw from our residential energy models?: guidelines for future modelling exercises. BuildSys@SenSys 2022: 315-319 - 2021
- [j14]Milad Leyli-Abadi, Allou Samé, Latifa Oukhellou
Online common change-point detection in a set of nonstationary categorical time series. Neurocomputing 439: 176-196 (2021) - [c26]Louise Bonfils, Allou Samé, Latifa Oukhellou
Dynamic clustering and modeling of temporal data subject to common regressive effects. ESANN 2021 - [c25]Etienne Côme
Machine learning and data mining for urban mobility intelligence. ESANN 2021
2010 – 2019
- 2019
- [j13]Milad Leyli-Abadi
Mixture of Joint Nonhomogeneous Markov Chains to Cluster and Model Water Consumption Behavior Sequences. ACM Trans. Intell. Syst. Technol. 10(6): 71:1-71:21 (2019) - [c24]Allou Samé, Milad Leyli-Abadi:
Change Point Detection in Periodic Panel Data Using a Mixture-Model-Based Approach. PAKDD (Workshops) 2019: 139-150 - [c23]Kevin Pasini, Mostepha Khouadjia, Allou Samé, Fabrice Ganansia, Latifa Oukhellou
LSTM Encoder-Predictor for Short-Term Train Load Forecasting. ECML/PKDD (3) 2019: 535-551 - 2018
- [j12]Ferhat Attal
Powered Two-Wheelers Critical Events Detection and Recognition Using Data-Driven Approaches. IEEE Trans. Intell. Transp. Syst. 19(12): 4011-4022 (2018) - [c22]Allou Samé:
Sequential Variational Learning of Dynamic Factor Mixtures. ICDM Workshops 2018: 682-688 - [c21]Milad Leyli-Abadi, Allou Samé, Latifa Oukhellou
Mixture of Non-homogeneous Hidden Markov Models for Clustering and Prediction of Water Consumption Time Series. IJCNN 2018: 1-8 - 2017
- [j11]Allou Samé, Gérard Govaert:
Segmental dynamic factor analysis for time series of curves. Stat. Comput. 27(6): 1617-1637 (2017) - [c20]Milad Leyli-Abadi, Allou Samé, Latifa Oukhellou
Predictive Classification of Water Consumption Time Series Using Non-homogeneous Markov Models. DSAA 2017: 323-331 - [c19]Nicolas Cheifetz, Allou Samé, Zineb Sabir, Anne-Claire Sandraz, Cédric Féliers:
Extracting urban water usage habits from smart meter data: a functional clustering approach. ESANN 2017 - 2016
- [j10]Hani El Assaad
A variational Expectation-Maximization algorithm for temporal data clustering. Comput. Stat. Data Anal. 103: 206-228 (2016) - [j9]Samer Mohammed, Allou Samé, Latifa Oukhellou
Recognition of gait cycle phases using wearable sensors. Robotics Auton. Syst. 75: 50-59 (2016) - [c18]Allou Samé:
Dynamic Factor Mixture of Experts for Functional Time Series Modeling. ICMLA 2016: 19-25 - [c17]Fateh Nassim Melzi, Taieb Touati, Allou Samé, Latifa Oukhellou:
Hourly Solar Irradiance Forecasting Based on Machine Learning Models. ICMLA 2016: 441-446 - 2015
- [c16]Fateh Nassim Melzi, Mohamed-Haykel Zayani, Amira Ben Hamida, François Stephan, Allou Samé, Latifa Oukhellou
Towards Smart City Energy Analytics: Identification of Consumption Patterns Based on the Clustering of Daily Electric Consumption Curves. CSDM 2015: 315 - [c15]Ferhat Attal, Abderrahmane Boubezoul, Allou Samé, Latifa Oukhellou:
Powered-Two-Wheeler safety critical events recognition using a mixture model with quadratic logistic functions. ESANN 2015 - [c14]Feteh Nassim Melzi, Mohamed-Haykel Zayani, Amira Ben Hamida, Allou Samé, Latifa Oukhellou:
Identifying Daily Electric Consumption Patterns from Smart Meter Data by Means of Clustering Algorithms. ICMLA 2015: 1136-1141 - 2014
- [c13]Allou Samé, Hani El Assaad:
A state-space approach to modeling functional time series application to rail supervision. EUSIPCO 2014: 1402-1406 - 2013
- [j8]Faicel Chamroukhi
Model-based functional mixture discriminant analysis with hidden process regression for curve classification. Neurocomputing 112: 153-163 (2013) - [c12]Hani El Assaad, Allou Samé, Gérard Govaert, Patrice Aknin
Model-Based Clustering of Temporal Data. ICANN 2013: 9-16 - [c11]Nicolas Cheifetz, Allou Samé, Patrice Aknin
A Sequential Testing Procedure for Multiple Change-Point Detection in a Stream of Pneumatic Door Signatures. ICMLA (1) 2013: 117-122 - [i8]Faicel Chamroukhi, Hervé Glotin, Allou Samé:
Model-based functional mixture discriminant analysis with hidden process regression for curve classification. CoRR abs/1312.6966 (2013) - [i7]Allou Samé, Faicel Chamroukhi, Gérard Govaert, Patrice Aknin:
Model-based clustering and segmentation of time series with changes in regime. CoRR abs/1312.6967 (2013) - [i6]Faicel Chamroukhi, Allou Samé, Gérard Govaert, Patrice Aknin:
A hidden process regression model for functional data description. Application to curve discrimination. CoRR abs/1312.6968 (2013) - [i5]Faicel Chamroukhi, Allou Samé, Gérard Govaert, Patrice Aknin:
Time series modeling by a regression approach based on a latent process. CoRR abs/1312.6969 (2013) - [i4]Faicel Chamroukhi, Allou Samé, Gérard Govaert, Patrice Aknin:
Modèle à processus latent et algorithme EM pour la régression non linéaire. CoRR abs/1312.6978 (2013) - [i3]Faicel Chamroukhi, Allou Samé, Gérard Govaert, Patrice Aknin:
A regression model with a hidden logistic process for signal parametrization. CoRR abs/1312.6994 (2013) - [i2]Faicel Chamroukhi, Allou Samé, Gérard Govaert, Patrice Aknin:
A regression model with a hidden logistic process for feature extraction from time series. CoRR abs/1312.7001 (2013) - [i1]Faicel Chamroukhi, Allou Samé, Patrice Aknin, Gérard Govaert:
Model-based clustering with Hidden Markov Model regression for time series with regime changes. CoRR abs/1312.7024 (2013) - 2012
- [c10]Nicolas Cheifetz, Allou Samé, Patrice Aknin, Emmanuel De Verdalle:
A CUSUM approach for online change-point detection on curve sequences. ESANN 2012 - [c9]Allou Samé, Gérard Govaert:
Online Time Series Segmentation Using Temporal Mixture Models and Bayesian Model Selection. ICMLA (1) 2012: 602-605 - [c8]Nicolas Cheifetz, Allou Samé, Patrice Aknin
A sequential testing approach for change-point detection on bus door systems. ITSC 2012: 1846-1851 - 2011
- [j7]Allou Samé, Faicel Chamroukhi
Model-based clustering and segmentation of time series with changes in regime. Adv. Data Anal. Classif. 5(4): 301-321 (2011) - [c7]Faicel Chamroukhi
Model-based clustering with Hidden Markov Model regression for time series with regime changes. IJCNN 2011: 2814-2821 - [c6]Nicolas Cheifetz, Allou Samé, Patrice Aknin
A pattern recognition approach for anomaly detection on buses brake system. ITSC 2011: 266-271 - 2010
- [j6]Faicel Chamroukhi
A hidden process regression model for functional data description. Application to curve discrimination. Neurocomputing 73(7-9): 1210-1221 (2010)
2000 – 2009
- 2009
- [j5]Faicel Chamroukhi
Time series modeling by a regression approach based on a latent process. Neural Networks 22(5-6): 593-602 (2009) - [c5]Faicel Chamroukhi, Allou Samé, Gérard Govaert, Patrice Aknin:
A regression model with a hidden logistic process for signal parametrization. ESANN 2009 - [c4]Faicel Chamroukhi
A regression model with a hidden logistic process for feature extraction from time series. IJCNN 2009: 489-496 - [c3]Allou Samé:
Grouped data clustering using a fast mixture-model-based algorithm. SMC 2009: 2276-2280 - 2007
- [j4]Allou Samé, Latifa Oukhellou
Mixture-model-based signal denoising. Adv. Data Anal. Classif. 1(1): 39-51 (2007) - [j3]Allou Samé, Laurent Bouillaut, Patrice Aknin, A. Ben Salem:
Réseaux bayésiens dynamiques à variable exogène continue pour la classification des points singuliers d'une voie ferrée. Rev. d'Intelligence Artif. 21(3): 353-370 (2007) - [j2]Allou Samé, Christophe Ambroise, Gérard Govaert:
An online classification EM algorithm based on the mixture model. Stat. Comput. 17(3): 209-218 (2007) - 2006
- [j1]Allou Samé, Christophe Ambroise, Gérard Govaert:
A classification EM algorithm for binned data. Comput. Stat. Data Anal. 51(2): 466-480 (2006) - 2005
- [c2]Allou Samé, Gérard Govaert, Christophe Ambroise:
A Mixture Model-Based On-line CEM Algorithm. IDA 2005: 373-384 - 2003
- [c1]Allou Samé, Christophe Ambroise, Gérard Govaert:
A Mixture Model Approach for Binned Data Clustering. IDA 2003: 265-274
Coauthor Index
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