论文标题
基于功能的间歇性预测组合：偏见，准确性和库存含义
Feature-based intermittent demand forecast combinations: bias, accuracy and inventory implications
论文作者
论文摘要
间歇性需求预测是生产系统和供应链管理中无处不在且具有挑战性的问题。近年来，从学术和实践角度来看，越来越关注为间歇性需求开发预测方法。但是，对预测组合方法的关注有限，这些方法在预测快速移动时间序列中实现了竞争性能。当前的研究旨在检查某些现有的预测组合方法的经验结果，并提出一个基于广义的特征框架，以进行间歇性预测。提出的框架已被证明可以根据两个实际数据集提高点和分位数预测的准确性。此外，还提供了一些特征，预测池和计算效率的分析。研究结果表明，拟议方法在间歇性需求预测中的清晰度和灵活性，并提供有关库存决策的见解。
Intermittent demand forecasting is a ubiquitous and challenging problem in production systems and supply chain management. In recent years, there has been a growing focus on developing forecasting approaches for intermittent demand from academic and practical perspectives. However, limited attention has been given to forecast combination methods, which have achieved competitive performance in forecasting fast-moving time series. The current study aims to examine the empirical outcomes of some existing forecast combination methods and propose a generalized feature-based framework for intermittent demand forecasting. The proposed framework has been shown to improve the accuracy of point and quantile forecasts based on two real data sets. Further, some analysis of features, forecasting pools and computational efficiency is also provided. The findings indicate the intelligibility and flexibility of the proposed approach in intermittent demand forecasting and offer insights regarding inventory decisions.
