一种避孕药具需求预测的新型混合方法：将点预测与概率分布相结合
A Novel Hybrid Approach to Contraceptive Demand Forecasting: Integrating Point Predictions with Probabilistic Distributions
AI总结：
本文提出一种结合特定领域模型点预测与统计机器学习概率分布的混合模型，通过引入专家知识微调系统预测，有效解决了发展中国家避孕药具需求预测中的数据不确定性问题。
摘要：
准确的需求预测对于确保可靠地获取避孕药具、支持采购、库存和分发等关键过程至关重要。然而，在发展中国家预测避孕药具需求面临诸多挑战，包括数据不完整、数据质量差，以及需要考虑多种地理和产品因素。当前方法通常依赖简单的预测技术，无法捕捉由这些因素引起的需求不确定性，需要专家介入。我们的研究旨在通过将概率预测方法与专家知识相结合来改进避孕药具需求预测。我们开发了一种混合模型，将来自特定领域模型的点预测与来自统计和机器学习方法的概率分布相结合，使人工输入能够微调并增强系统生成的预测。这种方法有助于解决需求中的不确定性，在资源有限的环境中尤为有用。我们评估了不同的预测方法，包括时间序列、贝叶斯、机器学习和基础时间序列方法，以及我们的新混合方法。通过比较这些方法，我们深入了解了它们的优势、劣势和计算要求。我们的研究填补了避孕药具需求预测方面的空白，并提供了一个结合算法和人类专业知识的实用框架。我们提出的模型也可以推广到具有类似数据模式的其他人道主义情境中。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
