具有正则化的集成预测用于从历史和当前预测中学习权重
Regularized Ensemble Forecasting for Learning Weights from Historical and Current Forecasts
AI总结：
本文提出了一种结合历史和当前预测的正则化集成方法，通过最小化方差并引入正则化项来优化权重，实验证明其在不同场景下优于传统模型。
摘要：
结合多个专家的预测结果通常比依赖单一专家能获得更准确的结果。在本文中，我们介绍了一种新的正则化集成方法，该方法通过利用当前预测和历史表现来设置权重，扩展了传统的线性意见池。与仅依赖当前预测或过去准确性的现有方法不同，我们的方法同时考虑了这两种来源。它通过最小化组合预测（或其变换版本）的方差（或其变换版本）来学习权重，同时结合由历史表现启发的正则化项。我们还展示了这种方法具有贝叶斯解释。在这一贝叶斯框架内不同的分布假设会导致方差成分和正则化项的不同功能形式，使方法能够适应各种场景。在对沃尔玛销售和宏观经济预测的实证研究中，我们的集成方法在专家的完整预测历史可用时以及专家进入和退出导致历史记录不完整时均优于领先的基准模型。在整个过程中，我们提供了示例，展示了最优权重是如何确定的，并基于实证结果讨论了该框架的优势以及何时专家的过去预测比当前预测更有信息量。
英文摘要：
Combining forecasts from multiple experts often yields more accurate results than relying on a single expert. In this paper, we focus on forecasting a continuous outcome and introduce a novel regularized ensemble method that extends the traditional linear opinion pool by leveraging both current forecasts and historical performances to set the weights. In contrast to existing approaches that emphasize either current forecasts or past accuracy, our method accounts for both sources simultaneously. It learns weights by minimizing the variance of the combined forecast (or its transformed version) while incorporating a regularization term informed by historical performances. We also show that this approach has a Bayesian interpretation. Different distributional assumptions within this Bayesian framework yield different functional forms for the variance component and the regularization term, adapting the method to various scenarios. In empirical studies on Walmart sales and macroeconomic forecasting, our ensemble outperforms leading benchmark models both when experts' full forecasting histories are available and when experts enter and exit over time. Throughout, we also characterize how the optimal weights depend on current forecasts and historical performance, and use the empirical results to discuss where the framework's strengths lie and when each source of information plays a greater role.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
