Forecast combination involves using multiple forecasts to create a single, more accurate prediction. Recently, feature-based forecasting has been employed to either select the most appropriate forecasting models or to learn the weights of their convex combination. In this paper, we present a multi-task learning methodology that simultaneously addresses both problems. This approach is implemented through a deep neural network with two branches: the regression branch, which learns the weights of various forecasting methods by minimizing the error of combined forecasts, and the classification branch, which selects forecasting methods with an emphasis on their diversity. To generate training labels for the classification task, we introduce an optimization-driven approach that identifies the most appropriate methods for a given time series. The proposed approach elicits the essential role of diversity in feature-based forecasting and highlights the interplay between model combination and model selection when learning forecasting ensembles. Experimental results on a large set of series from the M4 competition dataset show that our proposal enhances point forecast accuracy compared to state-of-the-art methods.

翻译：预测组合涉及利用多个预测结果生成单一、更准确的预测。近年来，基于特征的预测方法被用于选择最合适的预测模型，或学习其凸组合的权重。本文提出一种多任务学习方法，可同时解决这两个问题。该方法通过具有两个分支的深度神经网络实现：回归分支通过最小化组合预测误差来学习各预测方法的权重；分类分支则侧重于预测方法的多样性来进行选择。为生成分类任务的训练标签，我们引入了一种基于优化的方法，用于识别特定时间序列中最合适的预测方法。所提方法揭示了多样性在基于特征的预测中的关键作用，并强调了在构建预测集成时模型组合与模型选择之间的相互作用。在M4竞赛数据集的大规模时间序列上进行的实验表明，与现有最先进方法相比，本文方法能显著提升点预测精度。