Reconciled boosted models for GEFCom2017 hierarchical probabilistic load forecasting

Ben Taieb, 2014, A gradient boosting approach to the kaggle load forecasting competition, International Journal of Forecasting, 30, 382, 10.1016/j.ijforecast.2013.07.005 Ben Taieb, 2017 Chen, 2016, XGBoost: A scalable tree boosting system, 785 Chen, T., He, T., Benesty, M., Khotilovich, V., & Tang, Y. (2017). xgboost: Extreme gradient boosting. URL https://CRANR-project.org/package=xgboost, R package version 06-4. Friedman, 2001, Greedy function approximation: A gradient boosting machine, The Annals of Statistics, 29, 1189, 10.1214/aos/1013203451 Friedman, 2000, Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors), The Annals of Statistics, 28, 337, 10.1214/aos/1016218223 Gneiting, 2011, Quantiles as optimal point forecasts, International Journal of Forecasting, 27, 197, 10.1016/j.ijforecast.2009.12.015 Hong, 2010 Hong, 2014, Global energy forecasting competition 2012, International Journal of Forecasting, 30, 357, 10.1016/j.ijforecast.2013.07.001 Hong, 2016, Probabilistic energy forecasting: Global energy forecasting competition 2014 and beyond, International Journal of Forecasting, 32, 896, 10.1016/j.ijforecast.2016.02.001 Hong, 2019, Global energy forecasting competition 2017: Hierarchical probabilistic load forecasting, International Journal of Forecasting, 10.1016/j.ijforecast.2019.02.006 Hyndman, 2011, Optimal combination forecasts for hierarchical time series, Computational Statistics & Data Analysis, 55, 2579, 10.1016/j.csda.2011.03.006 Hyndman, 2010, Density forecasting for long-term peak electricity demand, IEEE Transactions on Power Systems, 25, 1142, 10.1109/TPWRS.2009.2036017 Hyndman, 2016, Fast computation of reconciled forecasts for hierarchical and grouped time series, Computational Statistics & Data Analysis, 97, 16, 10.1016/j.csda.2015.11.007 Koren, 2009, The BellKor solution to the Netflix grand prize Kuhn, 2017 R Core Team (2017). R: A language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria. URL https://www.R-project.org/. Schapire, 1990, The strength of weak learnability, Machine Learning, 5, 197, 10.1007/BF00116037 Wickramasuriya, 2015 Xie, 2018, Temperature scenario generation for probabilistic load forecasting, IEEE Transactions on Smart Grid, 9, 1680 Ziel, 2016, Lasso estimation for GEFCom2014 probabilistic electric load forecasting, International Journal of Forecasting, 32, 1029, 10.1016/j.ijforecast.2016.01.001