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This research investigates the efficacy of gradient boosting models specifically for intermittent demand forecasting, a niche challenge where standard machine learning approaches often struggle with sparse data. The study concludes that while specialized statistical methods outperform standalone gradient boosting, hybrid ensembles combining these techniques can significantly boost accuracy by up to 10%. These findings offer practical guidance for practitioners in supply chain and inventory management looking to optimize predictive models for low-frequency demand patterns.
Are Gradient Boosting Models Suitable for Intermittent Demand Forecasting?
arXiv:2609.14718v1 Announce Type: new Abstract: 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.
Title:Are Gradient Boosting Models Suitable for Intermittent Demand Forecasting?
Abstract: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.
Machine Learning (cs.LG)
arXiv:2609.14718 [cs.LG]
(or arXiv:2609.14718v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.14718 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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