Zhenzhen Chu
Weining Qian
ABSTRACT
Sales forecasting during the launch of new products has always been a challenging task, due to the lack of historical sales data. The dynamic market environment and consumer preferences also increase the uncertainty of predictions. Large chains face even greater difficulties due to their extensive presence across various regions. Traditional time-series forecasting methods usually rely on statistical models and empirical judgments, which are difficult to handle large, variable data and often fail to achieve satisfactory performance for new products. In this paper, we propose a Multi-granularity AdversaRial Learning framework (MARL) to leverage knowledge from old products and improve the quality of invariant representations for more accurate sales predictions. To evaluate our proposed method, we conducted extensive experiments on both a real-world dataset from a prominent international Café chain and a public dataset. The results demonstrated that our method is more effective than the existing state-of-the-art baselines for new product sales forecasting.
- Isabela Albuquerque, Jo ao Monteiro, Mohammad Darvishi, Tiago H Falk, and Ioannis Mitliagkas. 2020. Adversarial target-invariant representation learning for domain generalization. (2020).Google Scholar
- Lennart Baardman, Igor Levin, Georgia Perakis, and Divya Singhvi. 2017. Leveraging comparables for new product sales forecasting. Available at SSRN 3086237 (2017).Google Scholar
- Samaneh Beheshti-Kashi, Hamid Reza Karimi, Klaus-Dieter Thoben, Michael Lütjen, and Michael Teucke. 2015. A survey on retail sales forecasting and prediction in fashion markets. Systems Science & Control Engineering , Vol. 3, 1 (2015), 154--161.Google ScholarCross Ref
- Minghao Chen, Shuai Zhao, Haifeng Liu, and Deng Cai. 2020. Adversarial-learned loss for domain adaptation. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 3521--3528.Google ScholarCross Ref
- Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).Google Scholar
- Vijay Ekambaram, Kushagra Manglik, Sumanta Mukherjee, Surya Shravan Kumar Sajja, Satyam Dwivedi, and Vikas Raykar. 2020. Attention based multi-modal new product sales time-series forecasting. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 3110--3118.Google ScholarDigital Library
- Vivek F Farias, Srikanth Jagabathula, and Devavrat Shah. 2017. Building optimized and hyperlocal product assortments: A nonparametric choice approach. Available at SSRN 2905381 (2017).Google Scholar
- Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, Francc ois Laviolette, Mario Marchand, and Victor Lempitsky. 2016. Domain-adversarial training of neural networks. The journal of machine learning research , Vol. 17, 1 (2016), 2096--2030.Google Scholar
- Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2020. Generative adversarial networks. Commun. ACM, Vol. 63, 11 (2020), 139--144.Google ScholarDigital Library
- Hailin Hu, MingJian Tang, and Chengcheng Bai. 2020. Datsing: Data augmented time series forecasting with adversarial domain adaptation. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2061--2064.Google ScholarDigital Library
- Ye Huang, Wei Huang, Shiwei Tong, Zhenya Huang, Qi Liu, Enhong Chen, Jianhui Ma, Liang Wan, and Shijin Wang. 2021. STAN: Adversarial Network for Cross-domain Question Difficulty Prediction. In 2021 IEEE International Conference on Data Mining (ICDM). IEEE, 220--229.Google Scholar
- Kenneth B Kahn. 2014. Solving the problems of new product forecasting. Business Horizons, Vol. 57, 5 (2014), 607--615.Google ScholarCross Ref
- Tristan Karb, Niklas Kühl, Robin Hirt, and Varvara Glivici-Cotruta. 2020. A network-based transfer learning approach to improve sales forecasting of new products. arXiv preprint arXiv:2005.06978 (2020).Google Scholar
- Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).Google Scholar
- Haoliang Li, Sinno Jialin Pan, Shiqi Wang, and Alex C Kot. 2018. Domain generalization with adversarial feature learning. In Proceedings of the IEEE conference on computer vision and pattern recognition. 5400--5409.Google ScholarCross Ref
- Spyros Makridakis, Steven C Wheelwright, and Rob J Hyndman. 2008. Forecasting methods and applications. John wiley & sons.Google Scholar
- Toshihiko Matsuura and Tatsuya Harada. 2020. Domain generalization using a mixture of multiple latent domains. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 11749--11756.Google ScholarCross Ref
- Sinno Jialin Pan, Ivor W Tsang, James T Kwok, and Qiang Yang. 2010. Domain adaptation via transfer component analysis. IEEE transactions on neural networks , Vol. 22, 2 (2010), 199--210.Google Scholar
- Elizabeth Paton. 2018. H&M, a fashion giant, has a problem: $4.3 billion in unsold clothes. The New York Times (2018).Google Scholar
- Rui Shao, Xiangyuan Lan, Jiawei Li, and Pong C Yuen. 2019. Multi-adversarial discriminative deep domain generalization for face presentation attack detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 10023--10031.Google ScholarCross Ref
- Pawan Kumar Singh, Yadunath Gupta, Nilpa Jha, and Aruna Rajan. 2019. Fashion retail: Forecasting demand for new items. arXiv preprint arXiv:1907.01960 (2019).Google Scholar
- Geri Skenderi, Christian Joppi, Matteo Denitto, and Marco Cristani. 2021. Well googled is half done: Multimodal forecasting of new fashion product sales with image-based google trends. arXiv preprint arXiv:2109.09824 (2021).Google Scholar
- RM Steenbergen. 2019. New Product Forecasting with Analogous Products: Applying Random Forest and Quantile Regression Forest to forecasting and inventory management. Master's thesis. University of Twente.Google Scholar
- Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. 2016. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2818--2826.Google ScholarCross Ref
- Hui Tang and Kui Jia. 2020. Discriminative adversarial domain adaptation. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 5940--5947.Google ScholarCross Ref
- Rajesh Kumar Vashishtha, Vibhati Burman, Rajan Kumar, Srividhya Sethuraman, Abhinaya R Sekar, and Sharadha Ramanan. 2020. Product age based demand forecast model for fashion retail. arXiv preprint arXiv:2007.05278 (2020).Google Scholar
- Shanshan Wang and Lei Zhang. 2020. Self-adaptive re-weighted adversarial domain adaptation. arXiv preprint arXiv:2006.00223 (2020).Google Scholar
- Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio. 2015. Show, attend and tell: Neural image caption generation with visual attention. In International conference on machine learning. PMLR, 2048--2057.Google Scholar
- Chaohui Yu, Jindong Wang, Yiqiang Chen, and Meiyu Huang. 2019. Transfer learning with dynamic adversarial adaptation network. In 2019 IEEE International Conference on Data Mining (ICDM). IEEE, 778--786.Google ScholarCross Ref
- Yabin Zhang, Hui Tang, Kui Jia, and Mingkui Tan. 2019. Domain-symmetric networks for adversarial domain adaptation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 5031--5040.Google ScholarCross Ref
Index Terms
- Learning Invariant Representations for New Product Sales Forecasting via Multi-Granularity Adversarial Learning
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