Computer Science ›› 2019, Vol. 46 ›› Issue (6A): 446-451.

• Big Data & Data Mining • Previous Articles     Next Articles

Research on Sales Forecast of Prophet-LSTM Combination Model

GE Na1, SUN Lian-ying2, SHI Xiao-da1, ZHAO Ping1   

  1. Smart City College,Beijing Union University,Beijing 100101,China1;
    Urban Rail Transit and Logistics College,Beijing Union University,Beijing 100101,China2
  • Online:2019-06-14 Published:2019-07-02

Abstract: 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.

Key words: LSTM neural network, Prophet model, Sale forecast, Time series model

CLC Number: 

  • TP391
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