Title: E-commerce growth prediction model based on grey Markov chain

Authors: Pengfei Wang; Shuaijing Yu

Addresses: Yiwu Industrial and Commercial College, Yiwu, 322000, China ' Yiwu Industrial and Commercial College, Yiwu, 322000, China

Abstract: In order to solve the problems of long prediction consumption time and many prediction iterations existing in traditional prediction models, an e-commerce growth prediction model based on grey Markov chain is proposed. The Scrapy crawler framework is used to collect a variety of e-commerce data from e-commerce websites, and the feedforward neural network model is used to clean the collected data. With the cleaned e-commerce data as the input vector and the e-commerce growth prediction results as the output vector, an e-commerce growth prediction model based on the grey Markov chain is built. The prediction model is improved by using the background value optimisation method. After training the model through the improved particle swarm optimisation algorithm, accurate e-commerce growth prediction results are obtained. The experimental results show that the maximum time consumption of e-commerce growth prediction of this model is only 0.032, and the number of iterations is small.

Keywords: grey Markov chain; e-commerce; growth prediction; scrapy crawler framework; particle swarm optimisation.

DOI: 10.1504/IJEB.2024.141858

International Journal of Electronic Business, 2024 Vol.19 No.4, pp.362 - 379

Received: 10 Oct 2023
Accepted: 05 Dec 2023
Published online: 02 Oct 2024 *

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