文献类型：期刊文献

中文题名：Prophet-LSTM组合模型的销售量预测研究

英文题名：Research on Sales Forecast of Prophet-LSTM Combination Model

作者：葛娜[1];孙连英[2];石晓达[1];赵平[1]

第一作者：葛娜

机构：[1]北京联合大学智慧城市学院,北京100101;[2]北京联合大学城市轨道交通与物流学院,北京100101

第一机构：北京联合大学智慧城市学院

年份：2019

卷号：46

期号：B06

起止页码：446-451

中文期刊名：计算机科学

外文期刊名：Computer Science

收录：CSTPCD;;北大核心:【北大核心2017】；CSCD:【CSCD_E2019_2020】；

基金：国家重点研发计划(2016YFC0802107)资助

语种：中文

中文关键词：Prophet模型;长短时记忆神经网络;销售预测;时间序列模型

外文关键词：Prophet model;LSTM neural network;Sale forecast;Time series model

摘要：预测某种产品销售量的短期及长期变化趋势对企业制定营销战略和优化产业布局等具有重要的参考价值。在深入分析Prophet加法模型和长短时记忆神经网络的特性的基础上,依据某企业产品销量时间序列数据的趋势规律,构建了一种用于预测销售量的Prophet-LSTM神经网络组合模型,设计并实现了与组合前Prophet、LSTM单项模型及两种典型时间序列预测模型的对比实验。实验结果验证了Prophet-LSTM组合预测模型在销量时间序列分析中具有更强的适用性和更高的准确性,为该企业应对市场需求变化提供了重要的科学依据。
Predicting the short-term or long-term changes in the sales volume of a certain product has an important reference value for enterprises to formulate marketing strategies and optimize industrial layout.After deeply analyzing the characteristics of the Prophet additive model and the LSTM neural network,this paper built a Prophet-LSTM combinatorial model for forecasting sales based on the time-series data of a company's product sales.This paper designed and implemented comparison experiments with pre-combination Prophet,LSTM single-item model,and two typical time series prediction models.Experimental results show that the Prophet-LSTM combination forecasting model has stronger applicability and higher accuracy in the time series analysis of sales volume,which provides an important scientific basis for the company to respond to changes in market demand.