Abstract
Demand forecasting applications have immensely benefited from the state-of-the-art Deep Learning methods used for time series forecasting. Traditional uni-modal models are predominantly seasonality driven which attempt to model the demand as a function of historic sales along with information on holidays and promotional events. However, accurate and robust sales forecasting calls for accommodating multiple other factors, such as natural calamities, pandemics, elections, etc., impacting the demand for products and product categories in general. We propose a multi-modal sales forecasting network that combines real-life events from news articles with traditional data such as historical sales and holiday information. Further, we fuse information from general product trends published by Google trends. Empirical results show statistically significant improvements in the SMAPE error metric with an average improvement of 7.37% against the existing state-of-the-art sales forecasting techniques on a real-world supermarket dataset.
K. Dheenadayalan and N. Kumar—Both the authors have made equal contribution towards the paper.
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References
Armstrong, J.S., Green, K.C.: Demand forecasting: evidence-based methods. SSRN Electron. J. (2005)
Thomassey, S.: Sales forecasts in clothing industry: the key success factor of the supply chain management. Int. J. Prod. Econ. 128(2), 470–483 (2010). Supply Chain Forecasting Systems
Sawhney, R., Agarwal, S., Wadhwa, A., Shah, R.: Deep attentive learning for stock movement prediction from social media text and company correlations. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 8415–8426, November 2020
De Livera, A.M., Hyndman, R.J.: California Air Cleaning Units Mraket. Technical report, TechScience Research (2019)
Goyal, Y., Khot, T., Summers-Stay, D., Batra, D., Parikh, D.: Making the v in vqa matter: elevating the role of image understanding in visual question answering. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6325–6334 (2017)
Wang, W., Tran, D., Feiszli, M.: What makes training multi-modal classification networks hard? In :2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12692–12702, Los Alamitos, CA, USA (2020). IEEE
Owens, A., Efros, A.A.: Audio-Visual Scene Analysis with Self-Supervised Multisensory Features. arXiv e-prints, page arXiv:1804.03641 (2018)
Sen, R., Yu, H.-F., Dhillon, I.: Think globally, act locally: a deep neural network approach to high-dimensional time series forecasting (2019)
Chambers, R.D., Yoder, N.C.: Filternet: a many-to-many deep learning architecture for time series classification. Sensors 20(9) (2020)
Box, G.E.P., Jenkins, G.: Time Series Analysis, Forecasting and Control. Holden-Day Inc., USA (1990)
Taylor, S.J., Letham, B.: Forecasting at scale. Am. Stat. 72(1), 37–45 (2018)
Makridakis, S., Wheelwright, S.C., Hyndman, R.J.: Forecasting: Methods and Applications, 3rd ed. John Wiley & Sons, USA (1997)
Lim, B., Zohren, S.: Time series forecasting with deep learning: a survey. ArXiv, abs/2004.13408 (2020)
Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)
Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Kiranyaz, S., Avci, O., Abdeljaber, O., Ince, T., Gabbouj, M., Inman, D.J.: 1D convolutional neural networks and applications: a survey. Mech. Syst. Signal Process. 151, 107398 (2021)
Nassirtoussi, A.K., Aghabozorgi, S., Wah, T.Y., Ngo, D.C.L.: Text mining of news-headlines for forex market prediction. Expert Syst. Appl. 42(1), 306–324 (2015)
Hu, Z., Liu, W., Bian, J., Liu, X., Liu, T.Y.: Listening to chaotic whispers: a deep learning framework for news-oriented stock trend prediction. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, WSDM 2018, pp. 261–269, New York, NY, USA (2018)
Wang, W.Y., Hua, Z.: A semiparametric Gaussian copula regression model for predicting financial risks from earnings calls. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1155–1165, Baltimore, Maryland. Association for Computational Linguistics (2014)
Zisserman, A., et al.: The kinetics human action video dataset (2017)
Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Fei-Fei, L.: Large-scale video classification with convolutional neural networks. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1725–1732 (2014)
Li, Q., Tan, J., Wang, J., Chen, H.: A multimodal event-driven LSTM model for stock prediction using online news. IEEE Trans. Knowl. Data Eng. 1 (2020)
Mihalcea, R., Tarau, P.: TextRank: bringing order into text. In: Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, pp. 404–411, Barcelona, Spain. Association for Computational Linguistics (2004)
Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: Proceedings of the 31st International Conference on International Conference on Machine Learning, vol. 32, ICML 2014. JMLR.org (2014)
Kulkarni, G., Kannan, P.K., Moe, W.: Using online search data to forecast new product sales. Decis. Support Syst. 52(3), 604–611 (2012)
Schnaars, S.P.: Long-range forecasting: from crystal ball to computer: J. scott armstrong, 2nd ed. Wiley, New York (1985). [uk pound] 22.95 (paper), pp. 689. International Journal of Forecasting, 2(3), 387–390, 1986
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)
Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol. 30, pp. 4765–4774. Curran Associates Inc. (2017)
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Dheenadayalan, K., Kumar, N., Reddy, S., Kulkarni, S. (2023). Multimodal Neural Network for Demand Forecasting. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Lecture Notes in Computer Science, vol 13625. Springer, Cham. https://doi.org/10.1007/978-3-031-30111-7_35
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