Publications

J.-M. Poggi, Statistics and Machine Learning in Industry: combining heterogeneous or multi-scale model outputs, To appear in the book Statistical Methods and Applications in Systems Assurance & Quality of the Book Series Advanced Research in Reliability and System Assurance, Routledge and CRC Press, (2024) 1-9

E. Thulliez, B. Portier, M. Bobbia, J.-M. Poggi, Deployment of low-cost air quality sensors in Rouen: A dataset of one year of hourly concentrations of gas pollutants, Data in Brief, Volume 55 (2024) 110768.

M. Bobbia, J.-M. Poggi, B. Portier, E. Thulliez, Developing Voltage to Concentration Conversion Models for Low-Cost Sensors Measuring NO2, submitted, 16 p., 2024

A. Antoniadis, J. Cugliari, Y. Goude, M. Fasiolo, J.-M. Poggi, Statistical Learning Tools for Electricity Load Forecasting, in book series Statistics for Industry, Technology, and Engineering, Cham : Springer International Publishing, Birkhauser, 231 p., 2024

B. Auder, C. Coron, J.-.M. Poggi, E. Thulliez, Debiasing physico-chemical models in air quality monitoring by combining different pollutant concentration measures, Submitted, 1-17, 2024

M. Bourel, J. Cugliari, Y. Goude, J.-M. Poggi, Boosting Diversity in Regression Ensembles, Stat. Anal. Data Min.: ASA Data Sci. J. (2023), 1-17, https://doi.org/10.1002/sam.11654 

Y. Amara-Ouali, Y. Goude, J.-M. Poggi, Modelling the intensity of Electric Vehicle arrivals at charging points, Transportation Research Procedia, Proc. 8th International Electric Vehicle Conference (EVC 2023), 70 (2023) 372-379 

C. Gotwalt, R. Kenett, J.-M. Poggi, A journey in random forests and penalized regression in an industrial classification problem: How to use models to sharpen your questions, Quality and Reliability Engineering International, 1-16, 2023

E. Thulliez, M. Bobbia, J-M. Poggi, B. Portier, Air quality low-cost sensors and monitoring stations NO2 raw dataset in Rouen (France), Data in Brief, Vol. 49, 109398, 1-8, 2023 

B. Goehry, H. Yan, Y. Goude, P. Massart, J.-M. Poggi, Random Forests for Time Series, REVSTAT-Statistical Journal 21 (2), 283–302, 2023 

M. Bobbia, J.-M. Poggi, B. Portier, Spatial correction of low-cost sensors observations for statistical fusion of heterogeneous air quality measurements, Appl Stochastic Models Bus Ind., Volume 38(5), 766-786, 2022 

A. Bar Hen, S. Gey, J.-M. Poggi, Spatial CART Classification Trees, Computational Statistics, 36, 2591–2613 2021 

A. Antoniadis, S. Lambert-Lacroix, J.-M. Poggi, Random Forests for Global Sensitivity Analysis: a selective review, Reliability Engineering & System Safety, 1-14, vol. 206, 2021 

Y. Amara-Ouali, Y. Goude, P. Massart, J.-M. Poggi, H. Yan, A review of electric vehicle load open data and models, Energies, 14(8), 2233, 1-35, 2021

M. Bourel, J. Cugliari, Y. Goude, J.-M. Poggi, Generating experts by boosting diversity, Proceedings 63rd ISI World Statistics Congress, 11-16 July 2021, 1-6 

R. Genuer, J.-M. Poggi, Random Forests with R, 98 p., Use’R!, Springer, 2020 

E. Devijver, Y. Goude, J-M. Poggi, Clustering Electricity Consumers using High Dimensional Regression Mixture Models, Appl Stochastic Models Bus Ind. 36, 159-177, 2020 (link)

B. Goehry, Y. Goude, P. Massart, J-M. Poggi, Aggregation of Multi-scale Experts for Bottom-up Load Forecasting, IEEE Transactions on Smart Grid, vol. 11, 3, 1895-1904, 2020 

N. El Haouij, R. Ghozi, J-M. Poggi, S. Sevestre Ghalila, M. Jaïdane, Self-similarity Analysis of Electrodermal Activity for Stress Level Characterization, Quality and Reliability Engineering International, 35, 1502-1513, 2019 

R. Genuer, J.-M. Poggi, Les forêts aléatoires avec R, Editions PUR, coll. Pratique de la statistique, 112 pages, 2019 

B. Auder, J. Cugliari, Y. Goude, J-M. Poggi, Scalable Clustering of Individual Electrical Curves for Profiling and Bottom-up Forecasting, Energies, 2018, 11, 1893 

B. Auder, J. Cugliari, Y. Goude, J-M. Poggi, R package iecclust for Individual Electricity Customers data Clustering, Available on github 

R. Genuer, J.-M. Poggi, Arbres CART et Forêts aléatoires, Importance et sélection de variables, In Apprentissage Statistique et Données Massives, Maumy-Bertrand M., Saporta G. et Thomas Agnan C. (eds), Technip, p. 295-342, 2018 

N. El Haouij, J.-M. Poggi, R. Ghozi, S. Sevestre Ghalila, M. Jaïdane, Random Forest-Based Approach for Physiological Functional Variable Selection for Driver's Stress Level Classification, Statistical Methods & Applications, 1-29, 2018 

J. Cugliari, J.-M. Poggi, Electricity demand forecasting, Wiley StatsRef-Statistics Reference Online, 8 pages, 2018

R. Genuer, J-M. Poggi, C. Tuleau-Malot, N. Villa-Vialaneix, Random Forests for Big Data, Big Data Research, 9, 2017, 28-46 

N. El Haouij, J.-M. Poggi, R. Ghozi, S. Sevestre Ghalila, M. Jaïdane, AffectiveROAD System and Database to Assess Driver’s Arousal State, Proc. of the 33rd ACM Symposium on Applied Computing, SAC’18, April 9–13, 2018, Pau, France, 802-805 

R. Genuer, J.-M. Poggi, Arbres CART et Forêts aléatoires, Importance et sélection de variables. 45 pages, oct. 2016, arXiv:1610.08203 

A. Antoniadis, I. Gijbels, S. Lambert-Lacroix, J-M. Poggi, Joint estimation and variable selection for mean and dispersion in proper dispersion models, Electronic Journal of Statistics, Vol. 10, No. 1, 1630-1676, 2016 (link)

A. Bar-Hen, J-M. Poggi, Influence Measures and Stability for Graphical Models, Journal of Multivariate Analysis, Vol. 47, 145-154, 2016 

A. Antoniadis, X. Brossat, J. Cugliari, J.-M. Poggi, A prediction interval for a function-valued forecast model. Application to load forecasting. International Journal on Forecasting, 32(3), 939-947, 2016 

J. Cugliari, Y. Goude, J-M. Poggi, Disaggregated Electricity Forecasting using Wavelet-Based Clustering of Individual Consumers. Proceedings IEEE EnergyCon 2016, KU Leuven, 4-8 April 2016, 6 pages, 2016 

V. Thouvenot, A. Pichavant, A. Antoniadis, Y. Goude, J-M. Poggi, Electricity Forecasting using Multi-stage Estimators of Nonlinear Additive Models, IEEE Transactions on Power Systems, 31(5), 3665-3673, 2016 

B. Auder, M. Bobbia, J-M. Poggi, B. Portier, Sequential Aggregation of Heterogeneous Experts for PM10 Forecasting, Atmospheric Pollution Research, 7, 1101-1109, 2016 

J-M. Poggi, C. Bouveyron, G. Hébrail, F-X. Jollois, Un DU d’Analyste Big Data en formation continue courte, au niveau L3, Statistique et Enseignement, 7(1), 127-134, 2016 

A. Antoniadis, X. Brossat, Y. Goude, J-M. Poggi, V. Thouvenot, Automatic component selection in additive modeling of French national electricity load forecasting, In Nonparametric Statistics,191-209, Vol. 175, Springer Proceedings in Mathematics & Statistics, 2016 

R. Genuer, J.-M. Poggi, C. Tuleau-Malot, VSURF: Variable Selection Using Random Forests, The R Journal Vol. 7/2, 19-33, 2015

A. Antoniadis, J-M. Poggi, Discussion of "Analysis of Spatio-Temporal Mobile Phone Data: a Case Study in the Metropolitan Area of Milan", Statistical Methods & Applications, 1-6, 2015 

A. Antoniadis, Y. Goude, J-M. Poggi, V. Thouvenot, Sélection de variables dans les modèles additifs avec des estimateurs en plusieurs étapes, Technical report, HAL, hal-01116100, February 2015 

A. Antoniadis, J-M. Poggi, X. Brossat (Eds.), Preface to "Modeling and Stochastic Learning for Forecasting in High Dimensions", Lecture Notes in Statistics, Vol. 217, 2015

S. Gey, J.-M. Poggi, Discussion of Parallel Construction of Decision Trees with Consistently Non-Increasing Expected Number of Tests, Applied Stochastic Models in Business and Industry, Vol. 31(1), 2015, 79–80

A. Bar Hen, S. Gey, J.-M. Poggi, Influence measures for CART Classification Trees, Journal of Classification, 32(1), 21-45, 2015 

M. Misiti, Y. Misiti, J.M. Poggi, B. Portier, Mixture of linear regression models for short term PM10 forecasting in Haute Normandie (France), CS-BIGS, 6(1) 47-60, 2015 

M. Bobbia, M. Misiti, Y. Misiti, J.M. Poggi, B. Portier, Spatial outlier detection in the PM10 monitoring network of Normandy (France), Atmospheric Pollution Research, 6, 476-483, 2015 

A. Antoniadis, X. Brossat, J. Cugliari, J.-M. Poggi, Une approche fonctionnelle pour la prévision non-paramétrique de la consommation d'électricité, Journal de la SFdS, Vol. 155, No 2, 202-219, 2014 

J.-M. Poggi, R. S. Kenett, A. Pasanisi (Guest Editors), Editorial of the Special Issue on 2014 ENBIS-SFdS Spring Meeting "Graphical Causality Models: Trees, Bayesian Networks and Big Data", Quality Technology & Quantitative Management, Vol. 11, 1, 1-2, 2014 

R. Genuer, J.-M. Poggi, C. Tuleau-Malot, VSURF: Variable Selection Using Random Forests, R package, first version published may 2013 

J.-M. Poggi, Editorial Statistique et société, Vol. 1, No. 1, p. 5, mai 2013 

A. Bar Hen, S. Gey, J.-M. Poggi, Influence measures for CART Classification Trees, Proceedings 59th ISI World Statistics Congress, 25-30 August 2013, Hong Kong, 2132-2137, 2013 

A. Antoniadis, X. Brossat, J. Cugliari, J.-M. Poggi, Functional Clustering using Wavelets, International Journal of Wavelets, Multiresolution and Information Processing, Vol. 11, No. 1, 1350003 (30 pages), 2013 

A. Antoniadis, X. Brossat, J. Cugliari, J.-M. Poggi, Prévision d'un processus à valeurs fonctionnelles en présence de non stationnarités. Application à la consommation d'électricité, Journal de la Société Française de Statistique, Vol. 153, 2, 52-78, 2012 

F. Mhamdi, J.-M. Poggi, M. Jaidane, Forecasting time series through reconstructed multiple seasonal patterns using Empirical Mode Decomposition, Proceedings of the 20th International Conference on Computational Statistics, COMPSTAT2012, 11 pages, Limassol, Cyprus, 27-31 August 2012

M. Aminghafari, J.-M. Poggi, Nonstationary Time Series Forecasting Using Wavelets and Kernel Smoothing, Communications in Statistics - Theory and Methods, 41:3, 485-499, 2012 

J.-M. Poggi, B. Portier, PM10 forecasting using clusterwise regression, Atmospheric Environment, 45(38), 7005-7014, 2011

F. Mhamdi, J.-M. Poggi, M. Jaidane, Trend Extraction for Seasonal Time Series using Ensemble Empirical Mode Decomposition, Advances in Adaptive Data Analysis (AADA), 3(3), 363-383, 2011 

M. Bobbia, F.-X. Jollois, J.-M. Poggi, B. Portier, Quantifying local and background contributions to PM10 concentrations in Haute-Normandie, using random forests, Environmetrics, 22, 758-768, 2011

R. Genuer, J.-M. Poggi, C. Tuleau, Variable selection using Random Forests, Pattern Recognition Letters, 31(14), p. 2225-2236, 2010 

M. Misiti, Y. Misiti, G. Oppenheim, J.M. Poggi, Optimized Clusters for Disaggregated Electricity Load Forecasting, REVSTAT – Statistical Journal, 8(2), 105–124, 2010 

 A. Antoniadis, X. Brossat, J. Cugliari, J.-M. Poggi, Functional Clustering using Wavelets, Proceedings of the 19th COMPSTAT, Paris august 22-27, p. 697-704, 2010

F. Mhamdi, M. Jaidane, J.-M. Poggi, Empirical Mode Decomposition for Trend Extraction. Application to Electrical Data, Proceedings of the 19th COMPSTAT, Paris august 22-27, p. 1391-1398, 2010 

A. Antoniadis, I. Gijbels, J.-M. Poggi, Smoothing non equispaced heavy noisy data with wavelets, Statistica Sinica, 2009, Volume 19, Number 4, 19, 1371-1387, 2009 

F.-X. Jollois, J.-M. Poggi, B. Portier, Three non-linear statistical methods to analyze PM10 pollution in Rouen area, CSBIGS 3(1): 1-17, 2009 

N. Chèze, J.M. Poggi, Détection de données aberrantes en régression, Revue des Nouvelles Technologies de l’Information (RNTI), Cépaduès, 159-171, 2008 

M. Misiti, Y. Misiti, G. Oppenheim, J.M. Poggi, Optimized Clusters for Disaggregated Electricity Load Forecasting, Proceedings COMPSTAT 2008, Porto, Portugal, Physica Verlag, 225-232, 2008

R. Genuer, J.-M. Poggi, C. Tuleau, Random Forests: some methodological insights, Preprint INRIA, 2008 

M. Misiti, Y. Misiti, G. Oppenheim, J.M. Poggi, Wavelets and their applications, ISTE Publishing Knowledge, 352 p., june 2007

M. Misiti, Y. Misiti, G. Oppenheim, J.M. Poggi, Matlab Wavelet Toolbox (Version 4.0): Tutorial and Reference Guide, The Mathworks, Natick, USA, janv 2007

M. Misiti, Y. Misiti, G. Oppenheim, J.M. Poggi, Clustering signals using wavelets, Lecture Notes in Computer Science, 4507, F. Sandoval et al. (Eds.): IWANN 2007, 514-521, Springer, 2007 

J.M. Poggi, C. Tuleau, Classification of objectivization data using CART and wavelets, Proceedings of the IASC 07, Aveiro, Portugal, paper 18, 1-8, 2007

M. Amin Ghafari, J.M. Poggi, Forecasting time series using wavelets, International Journal of Wavelets, Multiresolution and Information Processing, vol. 5(5), 709-724, 2007 (link)

N. Chèze, J.M. Poggi, Outlier detection by boosting regression trees, Journal of Statistical Research of Iran (JSRI), 3, 1-21, 2006 (pdf)

S. Gey, J.M. Poggi, Boosting and Instability for regression trees, Computational Statistics and Data Analysis, 50, 533-550, 2006 (link)

N. Cheze, J.M. Poggi, Outlier detection by iterated boosting, Proceedings of the IFCS’06, Ljubljana, July 24-28, Data Science and Classification, Springer, 213-221, 2006 (link)

J.M. Poggi, C. Tuleau, Classification supervisée en grande dimension. Application à l’agrément de conduite automobile, Revue de Statistique Appliquée, LIV (4), 39-58, 2006 (pdf)

M. Amin Ghafari, N. Chèze, J.M. Poggi, Multivariate denoising using wavelets and principal components, Computational Statistics & Data Analysis 50, 2381- 2398, 2006 (link)

M. Misiti, Y. Misiti, G. Oppenheim, J.M. Poggi, Matlab Wavelet Toolbox (Version 3.0): Tutorial and Reference Guide, The Mathworks, Natick, USA, juin 2004

M. Misiti, Y. Misiti, G. Oppenheim, J.M. Poggi, Les ondelettes et leurs applications, Hermès, 336 p., février 2003

N. Chèze, J.M. Poggi, B. Portier, Partial and recombined estimators for nonlinear additive models, Statistical Inference for Stochastic Processes, vol. 6, 2, p. 155-197, 2003 

L. Bel, G. Oppenheim, J.M. Poggi, B. Portier, R. Vautard, Statistical forecasting of ozone peaks over Paris area with a nonlinear additive model. Impact of imported ozone, Proceedings of the 10th International Conference on "Applied Stochastic Models and Data Analysis", 166-171, Compiègne, June 12-15, 2001

J.M. Poggi, B. Portier, Asymptotic local test for linearity in adaptive control, Statistics and Probability Letters, vol. 55, 1, 9-18, 2001 

N. Chèze, J.M. Poggi, B. Portier, Partial estimators for nonlinear additive models: Motivation, Illustration and Case Study, Prépublication 2001-74, Orsay, 20 p., 2001 

N. Chèze, J.M. Poggi, B. Portier, Partial estimators for nonlinear additive models: Multivariate Central Limit Theorem and Test for Partial Additivity, Prépublication 2001-75, Orsay, 18 p., 2001

J.M. Poggi, B. Portier, Nonlinear adaptive tracking using kernel estimators: estimation and test for linearity, SIAM Journal in Control and Optimization, vol. 39, 707-727, 2000 

M. Misiti, Y. Misiti, G. Oppenheim, J.M. Poggi, Matlab Wavelet Toolbox (Version 2.0): Tutorial and Reference Guide, 520 p.+ 400p., The Mathworks, Natick, USA, nov. 2000

L. Bel, L. Bellanger, V. Bonneau, G. Ciuperca, D. Dacunha-Castelle, C. Deniau, B. Ghattas, M. Misiti, Y. Misiti, G. Oppenheim, J.M. Poggi, R. Tomassonne, Comparaison de prévisions de pics de pollution, Revue de Statistique Appliquée, vol. XLVII (3), 7-25, 1999 

J.M. Poggi, M.C. Viano, An estimate of the fractal index using multiscale aggregates, Journal of Time Series Analysis, vol. 19, 2, 221-233, 1998 

J.M. Poggi, B. Portier, Testing linearity for NARX models, European Journal of Control, vol. 4, 298-305, 1998 

M. Misiti, Y. Misiti, G. Oppenheim, J.M. Poggi, Méthodes d'ondelettes en statistique : introduction et exemples, Journal de la Société Française de Statistique, tome 139, n°4, 3-29, 1998 

N. Chèze-Payaud, J.M. Poggi, B. Portier, Estimation and test of linearity for a class of additive nonlinear models, Statistics and Probability Letters, 40, 189-201, 1998 

M. Misiti, Y. Misiti, G. Oppenheim, J.M. Poggi, Matlab Wavelet Toolbox (Version 1.0): Tutorial and Reference Guide, 600 p., The Mathworks, Natick, USA, mars 1996

J.M. Poggi, B. Portier, A test of linearity for functional autoregressive models, Journal of Time Series Analysis, vol. 18, 6, 615-639, 1997 

M. Misiti, Y. Misiti, G. Oppenheim, J.M. Poggi, Micronde: a Matlab Wavelet Toolbox for Signals and Images, Lecture Notes in Statistics, 103, Wavelets and Statistics, Springer Verlag, 239-260, 1995 (pdf)

J.M. Poggi, B. Portier, Un test de linéarité pour les modèles autorégressifs fonctionnels, C.R.A.S., t. 321, Ser. 1, 113-116, 1995

M. Misiti, Y. Misiti, G. Oppenheim, J.M. Poggi, Décomposition par ondelettes et méthodes comparatives : étude d'une courbe de charge électrique, Revue de Statistique Appliquée, vol. XLII (2), 57-77, 1994 

J.M. Poggi, Prévision non paramétrique de la consommation électrique, Revue de Statistique Appliquée, vol. XLII (4), 83-98, 1994 

M. Misiti, Y. Misiti, G. Oppenheim, J.M. Poggi, Analyse de signaux classiques par décomposition en ondelettes, Revue de Statistique Appliquée, vol. XLI (4), 5-32, 1993 

M. Misiti, Y. Misiti, G. Oppenheim, J.M. Poggi, Ondelettes en statistique et traitement du signal, Revue de Statistique Appliquée, vol. XLI (4), 33-43, 1993 

Y. Misiti, G. Oppenheim, J.M. Poggi, Représentation des connaissances dans les systèmes à base de règles et d'objets pour la Statistique, Revue de Statistique Appliquée, vol. XXXX (2), 99-106, 1992 (pdf)

Y. Misiti, J.M. Poggi, GEPETTO: An expert system for Computer Aided Control System Design, Actes de la Conférence Européenne d'Automatique ECC'91, 2226-2231, Grenoble, 2-5 juillet 1991, Hermès

R. Astier, Y. Misiti, G. Oppenheim, J.M. Poggi, C.A.O. et système expert en automatique: les phases d'amélioration dans la conception d'une loi de commande, Actes du congrès européen EUROPIA 88, 431-445, Paris, 28-29 nov. 1988, Editions Hermès, nov. 1988

Y. Misiti, J.M. Poggi, R. Astier, J. Dupont, G. Oppenheim, F.Y. Villemin, Système expert en conception de lois de pilotage, Actes des 7èmes Journées Internationales d'Avignon : Systèmes Experts et Applications, 1053-1075, juin 1987