There are two potential directions of forecast combination: combining for adaptation and combining for improvement. The former direction targets the performance of the best forecaster, while the latter attempts to combine forecasts to improve on the best forecaster. It is often useful to infer which goal is more appropriate so that a suitable combination method may be used. This paper proposes an AI-AFTER approach that can not only determine the appropriate goal of forecast combination but also intelligently combine the forecasts to automatically achieve the proper goal. As a result of this approach, the combined forecasts from AI-AFTER perform well universally in both adaptation and improvement scenarios. The proposed forecasting approach is implemented in our R package AIafter, which is available at https://github.com/weiqian1/AIafter.

预测组合有两个潜在的方向：适应组合和改进组合。前一个方向针对最佳预测器的性能，而后者尝试结合预测来改进最佳预测器。推断哪个目标更合适通常很有用，以便可以使用合适的组合方法。本文提出了一种AI-AFTER方法，该方法不仅可以确定预测组合的适当目标，还可以智能地组合预测以自动实现适当的目标。由于这种方法，来自 AI-AFTER 的组合预测在适应和改进场景中普遍表现良好。建议的预测方法在我们的 R 包AIafter 中实现，可在 https://github.com/weiqian1/AIafter 获得。