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Lead-lag grey forecasting model in the new community group buying retailing

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  • Zhu, Huimin
  • Xiao, Xinping
  • Kang, Yuxiao
  • Kong, Dekai

Abstract

Community group buying is the preferred shopping method for many consumers during the COVID-19 epidemic in China. This new Internet pre-sale mode has a lead- lag effect and non-linear characteristics. Therefore, studying the prediction of community group buying has practical significance. In this paper, the lead-lag term of vector autoregressive model is coupled with the nonlinear term in the GM (1, 1, tα) model. Then, lead-lag factor τ and nonlinear factor tμ are added in the grey modelling process, and a novel hybrid grey forecasting model of lead-lag and nonlinearity (GAPM) is established by means of optimising the background value. Firstly, the least squares method and derivation method are used to obtain the model parameter values and time response expressions, and the stability of GAPM is studied on the basis of condition number theory of matrices. Secondly, the grey evolutionary algorithm is introduced to obtain the optimal solution of the background value and nonlinear factor, and the weighted geometric average weakening buffer operator is used to preprocess the original data. Finally, the new model is used to fit and predict the daily sales of Orange Optimization Company in Wuhan, China for two consecutive months of community group buying. Results show that the model has good stability and high prediction accuracy. Compared with the other five common grey models that do not consider the lead-lag influence of the system, GAPM has better predictive ability. Therefore, the introduction of the new model constructed by using the lead-lag factor is reasonable and effective in the case of community group buying with lead-lag effect.

Suggested Citation

  • Zhu, Huimin & Xiao, Xinping & Kang, Yuxiao & Kong, Dekai, 2022. "Lead-lag grey forecasting model in the new community group buying retailing," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    • Handle: RePEc:eee:chsofr:v:158:y:2022:i:c:s096007792200234x
    DOI: 10.1016/j.chaos.2022.112024
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