Organizations rely on accurate demand forecasts to make production and ordering decisions in a variety of supply chain positions. Significant research in time series forecasting techniques and a variety of forecasting methods are available in the market. However, selecting the most accurate forecasting model for a given time series has become a complicated decision. Prior studies of forecasting methods have used either in-sample or out-of-sample performance as the basis for model selection procedures, but typically fail to incorporate both in their decision-making framework. In this research, we develop an expert system for time series forecasting model selection, using both relative in-sample performance and out-of-sample performance simultaneously to train classifiers. These classifiers are employed to automatically select the best performing forecasting model without the need for decision-maker intervention. The new model selection scheme bridges the gap between using in-sample and out-of-sample performance separately. The best performing model on the validation set is not necessarily selected by the expert system, since both in-sample and out-of-sample information are essential in the selection process. The performance of the proposed expert system is tested using the monthly dataset from the M3-Competition, and the results demonstrate an overall minimum of 20% improvement in the optimality gap comparing to the train/validation method. The new forecasting expert system is also applied to a real case study dataset obtained from MonarchFx (a distributed logistics solutions provider). This result demonstrates a robust predictive capability with lower mean squared errors, which allows organizations to achieve a higher level of accuracy in demand forecasts.

组织依靠准确的需求预测来在各种供应链位置上做出生产和订购决策。市场上对时间序列预测技术和各种预测方法进行了大量研究。但是，为给定的时间序列选择最准确的预测模型已成为一个复杂的决定。先前对预测方法的研究已使用样本内或样本外性能作为模型选择程序的基础，但通常无法将两者都纳入其决策框架。在这项研究中，我们开发了一个用于时间序列预测模型选择的专家系统，同时使用相对样本内性能和样本外性能来训练分类器。这些分类器用于自动选择性能最佳的预测模型，而无需决策者干预。新的模型选择方案弥合了分别使用样本内和样本外性能之间的差距。验证系统上性能最佳的模型不一定要由专家系统选择，因为样本内和样本外信息在选择过程中都是必不可少的。使用来自M3竞赛的每月数据集测试提出的专家系统的性能，结果表明，与训练/验证方法相比，最佳差距总体上提高了20％。新的预测专家系统还应用于从MonarchFx（分布式物流解决方案提供商）获得的真实案例研究数据集。