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28 December 2022 Research on retail demand forecasting based on deep learning
Shuai Mi, Jintian Ge, Hongmei Yan, Chunxin Dong
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Proceedings Volume 12506, Third International Conference on Computer Science and Communication Technology (ICCSCT 2022); 125066D (2022) https://doi.org/10.1117/12.2661780
Event: International Conference on Computer Science and Communication Technology (ICCSCT 2022), 2022, Beijing, China
Abstract
In the retail sector, accurate and efficient forecasting of sales for a wide range of products is a vital part of business operations, especially for subsequent sales optimization. This makes extremely high demands on forecasting methods or models. To enable more accurate predictions, we propose an AggDeepar model. Real sales data are used to make predictions. Experimentally, the AggDeepar model has greater applicability and higher accuracy in sales volume time series forecasting.
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Shuai Mi, Jintian Ge, Hongmei Yan, and Chunxin Dong "Research on retail demand forecasting based on deep learning", Proc. SPIE 12506, Third International Conference on Computer Science and Communication Technology (ICCSCT 2022), 125066D (28 December 2022); https://doi.org/10.1117/12.2661780
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KEYWORDS
Data modeling
Autoregressive models
Performance modeling
Distribution
Neural networks
Systems modeling
Machine learning
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