计算机科学
聚类分析
数据挖掘
数据聚合器
交易数据
骨料(复合)
灵活性(工程)
数据库事务
计量经济学
数据科学
作者
Maxime C. Cohen,Renyu Zhang,Kevin Jiao
出处
期刊:Social Science Research Network
[Social Science Electronic Publishing]
日期:2019-06-28
被引量:3
摘要
We study how retailers can use data aggregation and clustering to improve demand prediction. High accuracy in demand prediction allows retailers to more effectively manage their inventory and mitigate stock-outs and excess supply. A typical retail setting involves predicting demand for hundreds of items simultaneously. Although some items have a large amount of historical data, others were recently introduced, and thus, transaction data could be scarce. A common approach is to cluster several items and estimate a joint model at the cluster level. In this vein, one can estimate some model parameters by aggregating the data from several items and other parameters at the individual-item level. We propose a practical method referred to as data aggregation with clustering (DAC), which balances the tradeoff between data aggregation and model flexibility. DAC allows us to predict demand while optimally identifying the features that should be estimated at the (i) item, (ii) cluster, and (iii) aggregate levels. We show that the DAC algorithm yields a consistent estimate, along with improved asymptotic properties relative to the decentralized method, which estimates a different model for each item. Using both simulated and real data, we illustrate DAC's improvement in prediction accuracy relative to common benchmarks. Interestingly, the DAC algorithm has theoretical and practical advantages and helps retailers uncover meaningful managerial insights.
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