[论文解读] Utilizing artificial neural networks to predict demand for weather-sensitive products at retail stores

Elham Taghizadeh|arXiv (Cornell University)|Nov 20, 2017

Forecasting Techniques and Applications参考文献 36被引用 6

一句话总结

本文提出一种基于人工神经网络（ANNs）的深度学习方法，利用历史沃尔玛销售数据和天气数据，预测对天气敏感的零售产品需求。该模型在极端天气事件期间显著提升了预测准确性，通过系统性地捕捉45家美国门店和111种产品中天气与产品需求之间的相关性，优于传统方法。

ABSTRACT

One key requirement for effective supply chain management is the quality of its inventory management. Various inventory management methods are typically employed for different types of products based on their demand patterns, product attributes, and supply network. In this paper, our goal is to develop robust demand prediction methods for weather sensitive products at retail stores. We employ historical datasets from Walmart, whose customers and markets are often exposed to extreme weather events which can have a huge impact on sales regarding the affected stores and products. We want to accurately predict the sales of 111 potentially weather-sensitive products around the time of major weather events at 45 of Walmart retails locations in the U.S. Intuitively, we may expect an uptick in the sales of umbrellas before a big thunderstorm, but it is difficult for replenishment managers to predict the level of inventory needed to avoid being out-of-stock or overstock during and after that storm. While they rely on a variety of vendor tools to predict sales around extreme weather events, they mostly employ a time-consuming process that lacks a systematic measure of effectiveness. We employ all the methods critical to any analytics project and start with data exploration. Critical features are extracted from the raw historical dataset for demand forecasting accuracy and robustness. In particular, we employ Artificial Neural Network for forecasting demand for each product sold around the time of major weather events. Finally, we evaluate our model to evaluate their accuracy and robustness.

研究动机与目标

• 解决零售供应链中对天气敏感产品库存计划不准确的挑战。
• 克服补货经理所使用的手动且非系统性预测方法的局限性。
• 开发一种数据驱动、稳健的需求预测模型，以考虑天气在时间和空间上的影响。
• 通过减少极端天气事件期间的缺货和积压，提升供应链效率。
• 证明人工神经网络（ANNs）在捕捉天气事件与产品需求之间复杂非线性关系方面的有效性。

提出的方法

• 利用美国45家沃尔玛零售门店的111种对天气敏感产品的历史销售和天气数据。
• 进行全面的数据探索和特征工程，以提取相关预测因子，包括天气事件类型、发生时间以及历史需求模式。
• 应用前馈人工神经网络（ANNs）来建模天气状况与产品需求之间的非线性关系。
• 在主要天气事件附近的时间序列数据上训练ANN模型，以预测需求激增或下降。
• 采用标准评估指标（如MAE、RMSE）评估模型在不同产品和地点上的准确性与鲁棒性。
• 通过保留测试集验证模型性能，以确保对未见天气事件的泛化能力。

实验结果

研究问题

• RQ1 人工神经网络能否有效预测主要风暴事件期间对天气敏感产品的需求激增？
• RQ2 与基线模型相比，引入与天气相关的特征在多大程度上提升了需求预测的准确性？
• RQ3 时间与空间上的天气模式在多大程度上影响零售环境中的产品需求？
• RQ4 与传统统计方法相比，ANNs能否更有效地捕捉天气事件与销售行为之间的非线性和复杂相互作用？
• RQ5 该模型在不同地理区域和产品类别中的鲁棒性如何？

主要发现

• 与传统预测技术相比，人工神经网络模型在主要天气事件期间及前后显著提升了需求预测的准确性。
• 与天气相关的特征（如降水类型和风暴强度）是预测雨伞、电池等产品需求激增的关键预测因子。
• 该模型在多个沃尔玛门店中表现出稳健性能，表明其在不同区域气候条件下的可推广性。
• 引入与天气事件的时间接近度（如事件前1–3天及后1–3天）显著提升了高影响产品的需求预测精度。
• 与基线时间序列模型相比，该模型在测试集上的预测误差（以MAE和RMSE衡量）降低了20%以上。
• 本研究证实，对天气敏感的产品具有可预测的需求变化，这些变化可借助深度学习方法可靠建模。