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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References
Arnold, A., Liu, Y., Abe, N.: Temporal causal modeling with graphical granger methods. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 66–75. ACM (2007)
Box, G.E., Jenkins, G.M., Reinsel, G.C., Ljung, G.M.: Time Series Analysis: Forecasting and Control. Wiley, New York (2015)
Cao, L.-J., Tay, F.E.H.: Support vector machine with adaptive parameters in financial time series forecasting. IEEE Trans. Neural Netw. 14(6), 1506–1518 (2003)
Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Sci. Rep. 8(1), 6085 (2018)
Chen, X., Huang, J.Z., Luo, J.: Purtreeclust: a purchase tree clustering algorithm for large-scale customer transaction data. In: 2016 IEEE 32nd International Conference on Data Engineering (ICDE), pp. 661–672. IEEE (2016)
Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)
Elman, J.L.: Finding structure in time. Cogn. Sci. 14(2), 179–211 (1990)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Lai, G., Chang, W.-C., Yang, Y., Liu, H.: Modeling long-and short-term temporal patterns with deep neural networks. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 95–104. ACM (2018)
LeCun, Y., Bengio, Y., et al.: Convolutional networks for images, speech, and time series. In: The Handbook of Brain Theory and Neural Networks, vol. 3361, no. 10, p. 1995 (1995)
Lütkepohl, H.: New Introduction to Multiple Time Series Analysis. Springer Science & Business Media, Berlin, Heidelberg (2005). https://doi.org/10.1007/978-3-540-27752-1
Malhotra, P., Vig, L., Shroff, G., Agarwal, P.: Long short term memory networks for anomaly detection in time series. In: Proceedings, p. 89. Presses universitaires de Louvain (2015)
Seber, G.A., Lee, A.J.: Linear Regression Analysis, vol. 329. Wiley, New York (2012)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)
Vlachos, M., Yu, P., Castelli, V.: On periodicity detection and structural periodic similarity. In: Proceedings of the 2005 SIAM International Conference on Data Mining, pp. 449–460. SIAM (2005)
Yang, J., Nguyen, M.N., San, P.P., Li, X.L., Krishnaswamy, S.: Deep convolutional neural networks on multichannel time series for human activity recognition. In: Twenty-Fourth International Joint Conference on Artificial Intelligence (2015)
Yin, J., et al.: Experimental study of multivariate time series forecasting models. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 2833–2839 (2019)
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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