{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/attention-based-multi-modal-new-product-sales","title":"Attention based Multi-Modal New Product Sales Time-series Forecasting","arxiv_id":null,"date":"2020-08-23","proceeding":"ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2020 8","authors":["Vijay Ekambaram","Kushagra Manglik","Sumanta Mukherjee","SURYA SHRAVAN KUMAR SAJJA","Satyam Dwivedi","Vikas Raykar"],"abstract":"Trend driven retail industries such as fashion, launch substantial new products every season. In such a scenario, an accurate demand forecast for these newly launched products is vital for efficient downstream supply chain planning like assortment planning and stock allocation. While classical time-series forecasting algorithms can be used for existing products to forecast the sales, new products do not have any historical time-series data to base the forecast on. In this paper, we propose and empirically evaluate several novel attention-based multi-modal encoder-decoder models to forecast the sales for a new product purely based on product images, any available product attributes and also external factors like holidays, events, weather, and discount. We experimentally validate our approaches on a large fashion dataset and report the improvements in achieved accuracy and enhanced model interpretability as compared to existing k-nearest neighbor based baseline approaches.","url_abs":"https://dl.acm.org/doi/10.1145/3394486.3403362","url_pdf":"https://dl.acm.org/doi/pdf/10.1145/3394486.3403362","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"attention-based-multi-modal-new-product-sales","repo_url":"https://github.com/HumaticsLAB/AttentionBasedMultiModalRNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"new-product-sales-forecasting","task_name":"New Product Sales Forecasting"},{"task_slug":"short-observation-new-product-sales","task_name":"Short-observation new product sales forecasting"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"time-series-forecasting","task_name":"Time Series Forecasting"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/new-product-sales-forecasting-on-visuelle","task":"New Product Sales Forecasting","dataset":"VISUELLE","model":"Explainable  Cross-Attention Multimodal RNN","rank_in_archive_order":5,"of":5,"metrics":{"MAE":"32.1"},"uses_additional_data":false},{"leaderboard":"/sota/new-product-sales-forecasting-on-visuelle2-0","task":"New Product Sales Forecasting","dataset":"VISUELLE2.0","model":"Explainable Cross-Attention Multimodal RNN","rank_in_archive_order":1,"of":1,"metrics":{"MAE":"0.99"},"uses_additional_data":true},{"leaderboard":"/sota/short-observation-new-product-sales","task":"Short-observation new product sales forecasting","dataset":"VISUELLE2.0","model":"Explainable Cross-Attention Multimodal RNN","rank_in_archive_order":1,"of":1,"metrics":{"1 step MAE":"0.96","10 steps MAE":"0.94"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}