Forecasting model selection using intermediate classification: Application to MonarchFx corporation
作者：
Highlights：
• A new forecasting model selection based on intermediate classification is proposed.
• In the proposed algorithm, train and validation sets are used to train classifiers.
• Comparative out-of-sample performance is evaluated using trained classifiers.
• The results compared favorably to traditional out-of-sample performance models.
• The new selection method has been adopted and applied to data provided by MonarchFx.
摘要
•A new forecasting model selection based on intermediate classification is proposed.•In the proposed algorithm, train and validation sets are used to train classifiers.•Comparative out-of-sample performance is evaluated using trained classifiers.•The results compared favorably to traditional out-of-sample performance models.•The new selection method has been adopted and applied to data provided by MonarchFx.
论文关键词：Expert system,Time series forecasting,Model selection,Classification,Supply chain management
论文评审过程：Received 4 April 2019, Revised 7 March 2020, Accepted 9 March 2020, Available online 11 March 2020, Version of Record 19 March 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113371
