Additive models and robust aggregation for GEFCom2014 probabilistic electric load and electricity price forecasting

[1]  Rob J Hyndman,et al.  Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond , 2016 .

[2]  Jakub Nowotarski,et al.  Computing electricity spot price prediction intervals using quantile regression and forecast averaging , 2015, Comput. Stat..

[3]  Yannig Goude,et al.  Forecasting Electricity Consumption by Aggregating Experts; How to Design a Good Set of Experts , 2015 .

[4]  Gilles Stoltz,et al.  A second-order bound with excess losses , 2014, COLT.

[5]  Yannig Goude,et al.  Local Short and Middle Term Electricity Load Forecasting With Semi-Parametric Additive Models , 2014, IEEE Transactions on Smart Grid.

[6]  Gilles Stoltz,et al.  Forecasting electricity consumption by aggregating specialized experts , 2012, Machine Learning.

[7]  Trevor Hastie,et al.  Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.

[8]  A. Belloni,et al.  L1-Penalized Quantile Regression in High Dimensional Sparse Models , 2009, 0904.2931.

[9]  R. Weron,et al.  Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models , 2008 .

[10]  Gábor Lugosi,et al.  Prediction, learning, and games , 2006 .

[11]  S. Wood.  Generalized Additive Models: An Introduction with R , 2006 .

[12]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[13]  J. Friedman.  Stochastic gradient boosting , 2002 .

[14]  Manfred K. Warmuth,et al.  Exponentiated Gradient Versus Gradient Descent for Linear Predictors , 1997, Inf. Comput..

[15]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[16]  R. Tibshirani,et al.  Generalized additive models for medical research , 1995, Statistical methods in medical research.