Abstract
We introduce a novel regression model for time series forecasting in the cross-border e-commerce domain. In this paper, we present a new regression model for industry sale prediction (ISP) and hot product prediction (HPP) in the cross-border e-commerce domain. E-commerce products contain many attributes which may benefit to the final prediction performance. Based on this assumption, the proposed model employs a novel dual layer regression architecture to improve the generalization by capturing correlation between the historical data and future data, as well as enhancing the relationship of extracted features and target values. Besides, to verify the effectiveness of the proposed model, we establish two cross-border e-commerce datasets about imported lipsticks and shoes. The experimental results demonstrate that our proposed model achieves impressive results compared to a number of competitive baselines and the precision of hot product prediction reached 90%.
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Acknowledgments
This work was partially supported by Key Technologies Research and Development Program of Shenzhen JSGG20170817140856618, Shenzhen Foundational Research Funding JCYJ20180507183527919, National Natural Science Foundation of China 61632011, 61876053.
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Luo, W., Su, H., Liu, Y., Xu, R. (2020). A Dual Layer Regression Model for Cross-border E-commerce Industry Sale and Hot Product Prediction. In: Yang, Y., Yu, L., Zhang, LJ. (eds) Cognitive Computing – ICCC 2020. ICCC 2020. Lecture Notes in Computer Science(), vol 12408. Springer, Cham. https://doi.org/10.1007/978-3-030-59585-2_5
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