Application of metabolic GM (1,1,th,p) power model in fresh e-commerce prediction

Abstract

Aiming at the prediction problem of fresh e-commerce industry, this paper attempts to combine the metabolic theory with the improved GM (1,1) power model. The article introduces the traditional GM (1,1) power model to enhance the adaptability of the model to the data series, and at the same time, a new parameter is introduced when constructing the background value, the background value is represented as a linear function of adjacent sequence points, and the form and solution method of the two specific models are given, with the goal of minimizing the average relative error, and the power exponential and new parameters are co-optimized by genetic algorithm. The optimal modeling dimension is determined through model testing, and on this basis, the metabolic GM (1,1,th,p) power model is established, and the model is applied to the prediction of the transaction scale of the fresh e-commerce industry. The results show that the improved GM (1,1) power model can significantly improve the fitting accuracy compared with the original model, and the metabolic GM(1,1,th,p) power model has good performance in the prediction problem of fresh e-commerce.