Abstract
Accurate product sales forecasting, or known as demand forecasting, is important for retails to avoid either insufficient or excess inventory in product warehouse. Traditional works adopt either univariate time series models or multivariate time series models. Unfortunately, previous prediction methods frequently ignore the inherent structural information of product items such as the relations between product items and brands and the relations among various product items, and cannot perform accurate forecast. To this end, in this paper, we propose a deep learning-based prediction model, namely Structural Temporal Attention network (STANet), to adaptively capture the inherent inter-dependencies and temporal characteristics among product items. STANet uses the graph attention network and a variable-wise temporal attention to extract inter-dependencies among product items and to discover dynamic temporal characteristics, respectively. Evaluation on two real-world datasets validates that our model can achieve better results when compared with state-of-the-art methods.
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Acknowledgement
This work is partially supported by National Natural Science Foundation of China (Grant No. 61772371 and No. 61972286). We also would like to thank anonymous reviewers for their valuable comments.
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Liao, S., Yin, J., Rao, W. (2020). Towards Accurate Retail Demand Forecasting Using Deep Neural Networks. In: Nah, Y., Cui, B., Lee, SW., Yu, J.X., Moon, YS., Whang, S.E. (eds) Database Systems for Advanced Applications. DASFAA 2020. Lecture Notes in Computer Science(), vol 12114. Springer, Cham. https://doi.org/10.1007/978-3-030-59419-0_44
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DOI: https://doi.org/10.1007/978-3-030-59419-0_44
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