| 翻訳と辞書 |
| Quantile regression averaging ： ウィキペディア英語版 |
Quantile regression averaging
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 and originally used for probabilistic forecasting of electricity prices and loads. 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.
== Introduction ==
The individual point forecasts are used as independent variables and the corresponding observed target variable as the dependent variable in a standard quantile regression setting. The Quantile Regression Averaging method yields an interval forecast of the target variable, but does not use the prediction intervals of the individual methods. One of the reasons for using point forecasts (and not interval forecasts) is their availability. For years, forecasters have focused on obtaining accurate point predictions. Computing probabilistic forecasts, on the other hand, is generally a much more complex task and has not been discussed in the literature nor developed by practitioners so extensively. Therefore QRA may be found particularly attractive from a practical point of view as it allows to leverage existing development of point forecasting.
抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』
■ウィキペディアで「Quantile regression averaging」の詳細全文を読む
スポンサード リンク
| 翻訳と辞書 : 翻訳のためのインターネットリソース |
Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.
|
|