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KDD 2020 | Attention based multi-modal new product sales time-series forecasting

Accepted Papers

Attention based multi-modal new product sales time-series forecasting

Vijay Ekambaram: IBM Research; Kushagra Manglik: IBM Research; Sumanta Mukherjee: IBM Research; Surya Shravan Kumar Sajja: IBM Research; Satyam Dwivedi: IBM Research; Vikas Raykar: IBM Research


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.

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