发表机构
Gisma University of Applied Sciences（吉斯马应用科学大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文研究梯度提升模型在间歇性需求预测中的适用性，发现其单独表现不佳，但与专用方法结合可提升预测准确率最多10%，凸显了集成学习的价值。
AI 中文摘要
需求预测在现代工业中至关重要，通过改进库存管理，为降低成本并获得竞争优势提供了机会。然而，对于间歇性需求的产品，预测变得尤其具有挑战性，因为此类需求发生频率低，时间序列中包含大量零观测值。这种动态在多个行业部门中很常见，例如工业组织、消费品、航空、汽车和电子行业。受这些挑战的启发，本文探讨了梯度提升模型在提升预测性能方面的潜力。我们在多个数据集上评估了统计方法、专用方法、机器学习方法和集成方法。结果表明，在单个模型中，专用方法实现了最强的性能，而单独的梯度提升往往表现不佳。然而，将机器学习模型与专用方法相结合，可将预测准确率提高多达10%，这表明即使是简单的集成方法也能优于单一模型。总体而言，研究结果强调了将机器学习与特定领域的预测技术相结合以应对间歇性需求的价值。
英文摘要
Demand forecasting is critical in modern industry, offering opportunities to reduce costs and gain competitive advantage through improved inventory management. However, forecasting becomes particularly challenging for products with intermittent demand, where demand occurs infrequently and time series contain many zero observations. Such dynamics are common across diverse sectors, such as industrial organizations, consumer goods, aviation, automotive, and electronics. Motivated by these challenges, this paper explores the potential of gradient boosting models to improve forecasting performance. We evaluate statistical, specialized, machine learning, and ensemble approaches across multiple datasets. The results show that specialized methods achieve the strongest performance among individual models, while gradient boosting on its own tends to underperform. However, combining a machine learning model with a specialized approach improves forecasting accuracy by up to 10%, demonstrating that even simple ensembles can outperform single models. Overall, the findings highlight the value of combining machine learning with domain-specific forecasting techniques for intermittent demand.
