Research on the prediction of cross-border e-commerce sales based on stacking integrated learning

Tongyan Fu; Huiying Du

doi:10.1117/12.3009239

11 October 2023 Research on the prediction of cross-border e-commerce sales based on stacking integrated learning

Tongyan Fu, Huiying Du

Proceedings Volume 12918, Fourth International Conference on Computer Science and Communication Technology (ICCSCT 2023); 129182B (2023) https://doi.org/10.1117/12.3009239
Event: International Conference on Computer Science and Communication Technology (ICCSCT 2023), 2023, Wuhan, China

Abstract

In the context of the new era of rapid development of big data and artificial intelligence technologies, the use of data mining and algorithmic techniques to build sales prediction models with high accuracy rates can quickly help cross-border e-commerce (CBEC) enterprises to cope with the challenges related to data processing, feature extraction, and value information acquisition. Through the analysis and mining of raw data of CBEC transactions, a two-layer fusion model is built by using the stacking integrated learning method, with the Random Forest, XGBoost, and LightGBM algorithms as base learners and the support vector regression algorithm as a meta-learner, to predict CBEC sales and compare the prediction properties with the single model constructed by the base learner. The results show that the integrated learner trained by the stacking integrated learning method can improve the prediction performance of the single learner and performs best among all learners. The model can objectively reflect the status of CBEC sales and play an essential role in the company’s execution of strategic decisions such as merchandise sourcing and sales.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Tongyan Fu and Huiying Du "Research on the prediction of cross-border e-commerce sales based on stacking integrated learning", Proc. SPIE 12918, Fourth International Conference on Computer Science and Communication Technology (ICCSCT 2023), 129182B (11 October 2023); https://doi.org/10.1117/12.3009239

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