Quantile Regression Averaging (QRA) is a forecast combination approach to the computation of prediction intervals. It involves applying quantile regression to the point forecasts of a small number of individual forecasting models or experts. It has been introduced in 2014 by Jakub Nowotarski and Rafał Weron[1] and originally used for probabilistic forecasting of electricity prices[2][3] and loads.[4][5] Despite its simplicity it has been found to perform extremely well in practice - the top two performing teams in the price track of the Global Energy Forecasting Competition (GEFCom2014) used variants of QRA.[6][7]
^Weron, Rafał (2014). "Electricity price forecasting: A review of the state-of-the-art with a look into the future". International Journal of Forecasting. 30 (4). [Open Access]: 1030–1081. doi:10.1016/j.ijforecast.2014.08.008.
^Maciejowska, Katarzyna; Nowotarski, Jakub; Weron, Rafał (2016). "Probabilistic forecasting of electricity spot prices using Factor Quantile Regression Averaging". International Journal of Forecasting. 32 (3): 957–965. doi:10.1016/j.ijforecast.2014.12.004.
^Liu, B.; Nowotarski, J.; Hong, T.; Weron, R. (2015). "Probabilistic Load Forecasting via Quantile Regression Averaging on Sister Forecasts". IEEE Transactions on Smart Grid. PP (99): 1. doi:10.1109/TSG.2015.2437877. ISSN 1949-3053.
^Hong, Tao; Fan, Shu. "Probabilistic Electric Load Forecasting: A Tutorial Review". blog.drhongtao.com. Retrieved 2015-11-28.
^Gaillard, Pierre; Goude, Yannig; Nedellec, Raphaël (2016). "Additive models and robust aggregation for GEFCom2014 probabilistic electric load and electricity price forecasting". International Journal of Forecasting. 32 (3): 1038–1050. doi:10.1016/j.ijforecast.2015.12.001.
^Maciejowska, Katarzyna; Nowotarski, Jakub (2016). "A hybrid model for GEFCom2014 probabilistic electricity price forecasting" (PDF). International Journal of Forecasting. 32 (3): 1051–1056. doi:10.1016/j.ijforecast.2015.11.008.
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