Data Aggregation and Demand Prediction
提出一种名为数据聚合与聚类（DAC）的方法，帮助零售商在同时预测数百种商品需求时平衡数据聚合与模型灵活性，提高预测精度。
High accuracy in demand prediction allows retailers to effectively manage their inventory and mitigate stock-outs and excess supply. A typical retail setting involves predicting the demand for hundreds of items simultaneously, some with abundant historical data and others with scarce data. In “Data Aggregation and Demand Prediction,” Cohen, Zhang, and Jiao propose a novel practical method, called data aggregation with clustering (DAC), which balances the tradeoff between data aggregation and model flexibility. DAC empowers retailers to predict demand while optimally identifying the features that should be estimated at the item, cluster, and aggregate levels. Theoretically, DAC yields a consistent estimate, along with improved prediction errors relative to the benchmark that estimates a different model for each item. Practically, DAC yields a higher demand prediction accuracy relative to many common benchmarks using a real data set from a large online retailer.