ORION: Online Regularized Multi-task Regression and Its Application to Ensemble Forecasting
ORION: Online Regularized Multi-task Regression and Its Application to Ensemble Forecasting
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ORION：在线正则化多任务回归及其在集合预测中的应用
DOI：
10.1109/icdm.2014.90
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
L. Luo
中科院分区：
文献类型：
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作者：
Jianpeng Xu;P. Tan;L. Luo
包络预报是一种用于模拟非线性动态系统演化的著名数值预报技术。集合成员预报由计算机模型的多次运行生成，其中每次运行通过扰动起始条件或使用动态系统的不同模型表示来获得。集合平均值或中值通常被选为用于决策目的的聚合预测的共识点估计。这些方法是有限的，因为它们假设每个合奏成员都同样熟练，并且没有考虑它们的内在相关性。在本文中，我们铸造的合奏预测任务作为一个在线的，多任务的回归问题，并提出了一个框架，称为ORION估计的最佳权重组合合奏成员。当新的观测数据可用时，使用具有重新启动策略的新型在线学习来更新权重。在北美12个主要流域的季节性土壤湿度预测的实验结果表明，所提出的方法相比，合奏中位数和其他基线方法的优越性。
Ensemble forecasting is a well-known numerical prediction technique for modeling the evolution of nonlinear dynamic systems. The ensemble member forecasts are generated from multiple runs of a computer model, where each run is obtained by perturbing the starting condition or using a different model representation of the dynamic system. The ensemble mean or median is typically chosen as the consensus point estimate of the aggregated forecasts for decision making purposes. These approaches are limited in that they assume each ensemble member is equally skill ful and do not consider their inherent correlations. In this paper, we cast the ensemble forecasting task as an online, multi-task regression problem and present a framework called ORION to estimate the optimal weights for combining the ensemble members. The weights are updated using a novel online learning with restart strategy as new observation data become available. Experimental results on seasonal soil moisture predictions from 12 major river basins in North America demonstrate the superiority of the proposed approach compared to the ensemble median and other baseline methods.