Forecast customer flow using long short-term memory networks

机译：使用长期短期记忆网络预测客户流量

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摘要

Customer flow forecast is of practical importance in business intelligence domain. This paper particularly investigates an interesting issue, i.e., how to forecast off-line customer flow for over two thousand shops by considering both online customer behaviors and off-line periodic customer behaviors. Apparently, it is difficult to directly model these underlying dependent variables via traditional regression models. To this end, the proposed approach first introduces various extra information to incorporate more underlying factors. Then, the hierarchical linear model is performed to screen out insignificant factors. On the basis of this reduced feature space, the second-order flow factor is incorporated to model the variance term. The combined new feature set is then used for the learning of a number of Long Short Term Memory (LSTM) models. The rigorous experiments have been performed and the promising results demonstrate the superiority of the proposed approach which indicates the wide applicability of the proposed forecast model.

机译：客户流量预测在商业智能领域具有实际重要性。本文特别研究了一个有趣的问题，即如何通过考虑在线客户行为和离线定期客户行为来预测两千多家商店的离线客户流量。显然，很难通过传统回归模型直接对这些基础因变量进行建模。为此，所提出的方法首先引入了各种额外的信息以结合更多的潜在因素。然后，执行分层线性模型以筛选出不重要的因素。基于此减少的特征空间，并入了二阶流量因子以对方差项建模。然后，将组合的新功能集用于学习许多长期短期记忆（LSTM）模型。进行了严格的实验，令人鼓舞的结果证明了所提出方法的优越性，表明所提出的预测模型具有广泛的适用性。

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来源
《2017 International Conference on Security, Pattern Analysis, and Cybernetics》|2017年|61-66|共6页
会议地点 Shenzhen(CN)
作者
Zongming Yin; Junzhang Zhu; Xiaofeng Zhang;
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作者单位

School of Computer Science, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China, 518055;

School of Computer Science, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China, 518055;

School of Computer Science, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China, 518055;

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原文格式 PDF
正文语种 eng
中图分类
关键词
Predictive models; Data models; Feature extraction; Logic gates; Adaptation models; Security; Pattern analysis;

机译：预测模型;数据模型;特征提取;逻辑门;适应模型;安全性;模式分析;;

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4. Forecast customer flow using long short-term memory networks [C] . Zongming Yin, Junzhang Zhu, Xiaofeng Zhang International Conference on Security, Pattern Analysis, and Cybernetics . 2017

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5. Short-term streamflow forecasting using artificial neural networks. [D] . Zealand, Cameron Michael. 1997

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6. Correction: Empirical mode decomposition based long short-term memory neural network forecasting model for the short-term metro passenger flow [O] . Quanchao Chen, Di Wen, Xuqiang Li, 2020

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7. Empirical mode decomposition based long short-term memory neural network forecasting model for the short-term metro passenger flow [O] . Quanchao Chen, Di Wen, Xuqiang Li, 2019

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Forecast customer flow using long short-term memory networks

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