Censored Demand Estimation in Retail
Censored Demand Estimation in Retail
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DOI：
10.1145/3154489
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发表时间：
2017-12
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影响因子：
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通讯作者：
M. Amjad;Devavrat Shah
中科院分区：
文献类型：
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作者：
M. Amjad;Devavrat Shah
在本文中，感兴趣的问题是估计真实需求的产品在一个给定的商店位置和时间段在零售环境中的基础上一个单一的噪音和潜在的审查意见。为了解决这个问题，我们引入了一个%非参数框架，从多个时间序列进行推断。有些令人惊讶的是，我们建立的算法引入的目的是“矩阵完成”可以用来解决相关的推理问题。具体来说，使用通用奇异值保持（USVT）算法[7]，我们证明了我们的估计量是一致的：随着商店位置和时间间隔的数量增加到$\infty$，估计的平均需求相对于真实平均需求的平均均方误差变为0。我们建立自然吸引力的性质，所得到的估计分析，以及通过一系列的启发性模拟。使用零售业（沃尔玛）的真实的数据集，我们认为我们的方法的实际意义。
In this paper, the question of interest is estimating true demand of a product at a given store location and time period in the retail environment based on a single noisy and potentially censored observation. To address this question, we introduce a %non-parametric framework to make inference from multiple time series. Somewhat surprisingly, we establish that the algorithm introduced for the purpose of "matrix completion" can be used to solve the relevant inference problem. Specifically, using the Universal Singular Value Thresholding (USVT) algorithm [7], we show that our estimator is consistent: the average mean squared error of the estimated average demand with respect to the true average demand goes to 0 as the number of store locations and time intervals increase to $\infty$. We establish naturally appealing properties of the resulting estimator both analytically as well as through a sequence of instructive simulations. Using a real dataset in retail (Walmart), we argue for the practical relevance of our approach.