Papers › Attention based Multi-Modal New Product Sales Time-series Forecasting
Attention based Multi-Modal New Product Sales Time-series Forecasting
Vijay Ekambaram, Kushagra Manglik, Sumanta Mukherjee, SURYA SHRAVAN KUMAR SAJJA, Satyam Dwivedi, Vikas Raykar
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.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| New Product Sales Forecasting | VISUELLE | Explainable Cross-Attention Multimodal RNN | MAE | 32.1 | #5 of 5 | Archive leaderboard | report |
| New Product Sales Forecasting | VISUELLE2.0 | Explainable Cross-Attention Multimodal RNN | MAE | 0.99 | #1 of 1 | Archive leaderboard | report |
| Short-observation new product sales forecasting | VISUELLE2.0 | Explainable Cross-Attention Multimodal RNN | 1 step MAE | 0.96 | #1 of 1 | Archive leaderboard | report |
| Short-observation new product sales forecasting | VISUELLE2.0 | Explainable Cross-Attention Multimodal RNN | 10 steps MAE | 0.94 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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