J. Y. Audibert, Fast learning rates in statistical inference through aggregation, The Annals of Statistics, vol.37, issue.4, pp.1591-1646, 2009.
DOI : 10.1214/08-aos623

URL : https://hal.archives-ouvertes.fr/hal-00139030

G. Biau and B. Patra, Sequential quantile prediction of time series. Information Theory, IEEE Transactions on, vol.57, issue.3, pp.1664-1674, 2011.
DOI : 10.1109/tit.2011.2104610

URL : https://hal.archives-ouvertes.fr/hal-00606486

G. W. Brier, Verification of forecasts expressed in terms of probability, Monthly Weather Review, vol.78, issue.1, pp.1-3, 1950.

J. Bröcker, Reliability, sufficiency, and the decomposition of proper scores, Quarterly Journal of the Royal Meteorological Society, vol.135, issue.643, pp.1512-1519, 2009.

J. Bröcker, Evaluating raw ensembles with the continuous ranked probability score, Quarterly Journal of the Royal Meteorological Society, vol.138, issue.667, pp.1611-1617, 2012.

J. Bröcker and L. A. Smith, Scoring probabilistic forecasts: The importance of being proper, Weather and Forecasting, vol.22, issue.2, pp.382-388, 2007.

G. Candille and O. Talagrand, Evaluation of probabilistic prediction systems for a scalar variable, Quarterly Journal of the Royal Meteorological Society, vol.131, issue.609, pp.2131-2150, 2005.

O. Catoni, Statistical learning theory and stochastic optimization, Lectures on probability theory and statistics, Ecole d'´ eté de Probabilités de Saint-Flour XXXI-2001, Lecture Notes in Mathematics, vol.1851, pp.1-269, 2004.

N. Cesa-bianchi and G. Lugosi, Prediction, learning, and games, 2006.

R. T. Clemen and R. L. Winkler, Combining probability distributions from experts in risk analysis, Risk analysis, vol.19, issue.2, pp.187-203, 1999.
DOI : 10.1111/j.1539-6924.1999.tb00399.x

A. Dawid, Comments on: Assessing probabilistic forecasts of multivariate quantities, with an application to ensemble predictions of surface winds, TEST, vol.17, issue.2, pp.243-244, 2008.

M. Devaine, P. Gaillard, Y. Goude, and G. Stoltz, Forecasting electricity consumption by aggregating specialized experts, Machine Learning, vol.90, issue.2, pp.231-260, 2013.
DOI : 10.1007/s10994-012-5314-7

URL : https://hal.archives-ouvertes.fr/hal-00484940

E. S. Epstein, A scoring system for probability forecasts of ranked categories, Journal of Applied Meteorology and Climatology, vol.8, issue.6, pp.985-987, 1969.

C. Ferro, Fair scores for ensemble forecasts, Quarterly Journal of the Royal Meteorological Society, vol.140, issue.683, pp.1917-1923, 2014.
DOI : 10.1002/qj.2270

URL : https://ore.exeter.ac.uk/repository/bitstream/10871/20641/2/Fair%20scores%20for%20ensemble%20forecasts.pdf

C. Ferro, D. S. Richardson, and A. P. Weigel, On the effect of ensemble size on the discrete and continuous ranked probability scores, Meteorological Applications, vol.15, issue.1, pp.19-24, 2008.

C. Fraley, A. E. Raftery, and T. Gneiting, Calibrating multimodel forecast ensembles with exchangeable and missing members using bayesian model averaging, Monthly Weather Review, vol.138, issue.1, pp.190-202, 2010.
DOI : 10.1175/2009mwr3046.1

URL : http://www.stat.washington.edu/research/reports/2009/tr556.pdf

T. E. Fricker, C. Ferro, and D. B. Stephenson, Three recommendations for evaluating climate predictions, Meteorological Applications, vol.20, issue.2, pp.246-255, 2013.
DOI : 10.1002/met.1409

URL : https://ore.exeter.ac.uk/repository/bitstream/10871/20991/1/Three%20recommendations%20for%20evaluating%20climate%20predictions.pdf

C. Genest and K. J. Mcconway, Allocating the weights in the linear opinion pool, Journal of Forecasting, vol.9, issue.1, pp.53-73, 1990.
DOI : 10.1002/for.3980090106

T. Gneiting and M. Katzfuss, Probabilistic forecasting, Annual Review of Statistics and Its Application, vol.1, pp.125-151, 2014.

T. Gneiting and A. E. Raftery, Strictly proper scoring rules, prediction, and estimation, Journal of the American Statistical Association, vol.102, issue.477, pp.359-378, 2007.
DOI : 10.21236/ada459827

URL : http://www.dtic.mil/dtic/tr/fulltext/u2/a459827.pdf

T. Gneiting, A. E. Raftery, I. Westveld, . Ah, and T. Goldman, Calibrated probabilistic forecasting using ensemble model output statistics and minimum crps estimation, Monthly Weather Review, vol.133, issue.5, pp.1098-1118, 2005.
DOI : 10.1175/mwr2904.1

URL : http://www.stat.washington.edu/raftery/Research/PDF/gneiting2005.pdf

I. J. Good, Rational decisions, Journal of the Royal Statistical Society. Series B (Methodological, pp.107-114, 1952.

E. P. Grimit, T. Gneiting, V. J. Berrocal, and N. A. Johnson, The continuous ranked probability score for circular variables and its application to mesoscale forecast ensemble verification, Quarterly Journal of the Royal Meteorological Society, vol.132, issue.621C, pp.2925-2942, 2006.

H. Hersbach, Decomposition of the continuous ranked probability score for ensemble prediction systems, Weather and Forecasting, vol.15, issue.5, pp.559-570, 2000.

C. Junk, L. Delle-monache, and S. Alessandrini, Analog-based ensemble model output statistics, Monthly Weather Review, 2015.
DOI : 10.1175/mwr-d-15-0095.1

J. Kivinen and M. K. Warmuth, Exponentiated Gradient versus Gradient Descent for Linear Predictors, Information and Computation, vol.132, issue.1, pp.1-63, 1997.
DOI : 10.1006/inco.1996.2612

URL : https://doi.org/10.1006/inco.1996.2612

M. Leutbecher and T. N. Palmer, Ensemble forecasting, Journal of Computational Physics, vol.227, issue.7, pp.3515-3539, 2008.

J. M. Lewis, Roots of ensemble forecasting, Monthly weather review, vol.133, issue.7, pp.1865-1885, 2005.

V. Mallet, Ensemble forecast of analyses: Coupling data assimilation and sequential aggregation, Journal of Geophysical Research, vol.115, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00547903

V. Mallet, B. Mauricette, and G. Stoltz, Description of sequential aggregation methods and their performances for ozone ensemble forecasting, 2007.

V. Mallet, G. Stoltz, and B. Mauricette, Ozone ensemble forecast with machine learning algorithms, 2009.
URL : https://hal.archives-ouvertes.fr/inria-00565770

A. H. Murphy, A note on the ranked probability score, Journal of Applied Meteorology and Climatology, vol.10, issue.2, pp.155-156, 1971.

F. Orabona, K. Crammer, and N. Cesa-bianchi, A generalized online mirror descent with applications to classification and regression, Machine Learning, vol.99, issue.3, pp.411-435, 2015.

A. E. Raftery, T. Gneiting, F. Balabdaoui, and M. Polakowski, Using Bayesian model averaging to calibrate forecast ensembles, Monthly Weather Review, vol.133, issue.1, p.174, 2005.

R. Ranjan and T. Gneiting, Combining probability forecasts, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.72, issue.1, pp.71-91, 2010.

L. J. Savage, Elicitation of personal probabilities and expectations, Journal of the American Statistical Association, vol.66, issue.336, pp.783-801, 1971.

G. Stoltz, Agrégation séquentielle de prédicteurs : méthodologie générale et applicationsàapplicationsà la prévision de la qualité de l'air etàetà celle de la consommationélectrique consommationélectrique, Journal de la Société Française de Statistique, vol.151, issue.2, pp.66-106, 2010.

V. Vovk and F. Zhdanov, Prediction with expert advice for the brier game, J. Mach. Learn. Res, vol.10, pp.2445-2471, 2009.

R. L. Winkler and A. H. Murphy, good" probability assessors, Journal of applied Meteorology, vol.7, issue.5, pp.751-758, 1968.
DOI : 10.1175/1520-0450(1968)007<0751:pa>2.0.co;2

URL : http://journals.ametsoc.org/doi/pdf/10.1175/1520-0450%281968%29007%3C0751%3APA%3E2.0.CO%3B2

S. Yitzhaki and E. Schechtman, The gini methodology: A primer on a statistical methodology, vol.272, 2012.