A Novel Hybrid Approach to Contraceptive Demand Forecasting: Integrating Point Predictions with Probabilistic Distributions
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避孕产品需求预测的混合方法：结合点预测与概率分布
- 研究问题
- 在数据缺失、质量有限且地区与产品差异明显的情境下，如何结合算法和专家知识，预测避孕产品需求及其不确定性？
- 方法与思路
- 作者将领域模型的点预测与统计、机器学习方法的概率分布结合，允许人工调整，并比较多类预测方法及其计算需求。
- 材料陈述的贡献
- 摘要称，该混合框架整合算法与人类经验，为资源有限环境中的需求预测提供实用方案，并提出向类似人道援助场景推广的可能。
- 阅读关注点
- 应核对点预测与概率分布的结合规则、专家调整机制，以及评估数据、预测期限、概率校准、比较指标和计算成本。
- 阅读边界
- 这是 arXiv 预印本，同行评审及接收状态未核实。摘要未给出数据规模、评估数值或外部验证，不能确认改进幅度和推广能力。
原文摘要
Accurate demand forecasting is vital for ensuring reliable access to contraceptive products, supporting key processes like procurement, inventory, and distribution. However, forecasting contraceptive demand in developing countries presents challenges, including incomplete data, poor data quality, and the need to account for multiple geographical and product factors. Current methods often rely on simple forecasting techniques, which fail to capture demand uncertainties arising from these factors, warranting expert involvement. Our study aims to improve contraceptive demand forecasting by combining probabilistic forecasting methods with expert knowledge. We developed a hybrid model that combines point forecasts from domain-specific model with probabilistic distributions from statistical and machine learning approaches, enabling human input to fine-tune and enhance the system-generated forecasts. This approach helps address the uncertainties in demand and is particularly useful in resource-limited settings. We evaluate different forecasting methods, including time series, Bayesian, machine learning, and foundational time series methods alongside our new hybrid approach. By comparing these methods, we provide insights into their strengths, weaknesses, and computational requirements. Our research fills a gap in forecasting contraceptive demand and offers a practical framework that combines algorithmic and human expertise. Our proposed model can also be generalized to other humanitarian contexts with similar data patterns.