A functional wavelet?kernel approach for time series prediction, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.42, issue.5, pp.837-857, 2006. ,
DOI : 10.1073/pnas.42.1.43
CLUSTERING FUNCTIONAL DATA USING WAVELETS, Proceedings of the Nineteenth International Conference on Computational Statistics (COMPSTAT), 2010. ,
DOI : 10.1142/S0219691313500033
URL : https://hal.archives-ouvertes.fr/hal-00853949
Adaptive and Self-Confident On-Line Learning Algorithms, Journal of Computer and System Sciences, vol.64, issue.1, pp.48-75, 2002. ,
DOI : 10.1006/jcss.2001.1795
Empirical support for Winnow and Weighted-Majority based algorithms: results on a calendar scheduling domain, Machine Learning, pp.5-23, 1997. ,
DOI : 10.1016/B978-1-55860-377-6.50017-7
From External to Internal Regret, Journal of Machine Learning Research, vol.8, pp.1307-1324, 2007. ,
DOI : 10.1007/11503415_42
On the competitive theory and practice of portfolio selection, Proceedings of the Fourth Latin American Symposium on Theoretical Informatics (LATIN'00), pp.173-196, 2000. ,
A non-linear regression model for mid-term load forecasting and improvements in seasonnality, Proceedings of the Fifteenth Power Systems Computation Conference (PSCC), 2005. ,
Comparative Models for Electrical Load Forecasting, 1985. ,
Potential-based algorithms in on-line prediction and game theory, Machine Learning, pp.239-261, 2003. ,
Improved second-order inequalities for prediction under expert advice, Machine Learning, pp.321-352, 2007. ,
Universal Portfolios, Mathematical Finance, vol.9, issue.1, pp.1-29, 1991. ,
DOI : 10.1016/0378-4266(79)90023-2
An empirical comparison of algorithms for aggregating expert predictions, Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence (UAI), 2006. ,
Time series prediction with performance guarantee, IET Communications, vol.5, issue.8, pp.1044-1051, 2011. ,
DOI : 10.1049/iet-com.2010.0121
Learning the switching rate by discretising Bernoulli sources online, Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS), 2009. ,
Aggregation of sleeping predictors to forecast electricity consumption, 2009. ,
An hourly periodic state space model for modelling French national electricity load, International Journal of Forecasting, vol.24, issue.4, pp.566-587, 2008. ,
DOI : 10.1016/j.ijforecast.2008.08.010
Using and combining predictors that specialize, Proceedings of the twenty-ninth annual ACM symposium on Theory of computing , STOC '97, pp.334-343, 1997. ,
DOI : 10.1145/258533.258616
A further look at the forecasting of the electricity consumption by aggregation of specialized experts, 2011. ,
A further look at sequential aggregation rules for ozone ensemble forecasting, 2008. ,
Mélange de prédicteurs et applicationàapplication`applicationà la prévision de consommationélectriqueconsommationélectrique, 2008. ,
Tracking the best predictor with a detection based algorithm, Proceedings of the Joint Statistical Meetings (JSP), 2008b. See the section on Statistical Computing ,
Tracking the best expert, Machine Learning, pp.151-178, 1998. ,
Adapting to non-stationarity with growing predictor ensembles, 2011. ,
Regret bounds for sleeping experts and bandits, Proceedings of the Twenty-First Annual Conference on Learning Theory (COLT), pp.425-436, 2008. ,
DOI : 10.1007/s10994-010-5178-7
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.143.6257
Ensemble forecast of analyses: Coupling data assimilation and sequential aggregation, Journal of Geophysical Research: Atmospheres, vol.113, issue.D22, 2010. ,
DOI : 10.1029/2008JD009991
URL : https://hal.archives-ouvertes.fr/inria-00547903
Ozone ensemble forecast with machine learning algorithms, Journal of Geophysical Research, vol.41, issue.1, 2009. ,
DOI : 10.1029/2008JD009978
URL : https://hal.archives-ouvertes.fr/inria-00565770
Online learning of non-stationary sequences, Advances in Neural Information Processing Systems (NIPS), pp.1093-1100, 2003. ,
Tracking climate models, Statistical Analysis and Data Mining, vol.7, issue.4, pp.372-392, 2011. ,
DOI : 10.1002/sam.10126
Short-term electricity load forecasting with generalized additive models, Proceedings of the Sixteenth International Conference on Intelligent System Application to Power Systems (ISAP), 2011. ,
Short-term electricity load forecasting with generalized additive models, Proceedings of the Third International Conference on Computational and Financial Econometrics, 2009. ,
Internal regret in on-line portfolio selection, Machine Learning, pp.125-159, 2005. ,
URL : https://hal.archives-ouvertes.fr/hal-00007535
Prediction with expert advice for the Brier game, Proceedings of the 25th international conference on Machine learning, ICML '08, 2008. ,
DOI : 10.1145/1390156.1390295
Generalized Additive Models: An Introduction with R. Chapman and Hall/CRC, 2006. ,