Retail demand forecasting has long been built on time series models that require historical sales for every SKU. In this talk, we trace the evolution of time series methods—from ARIMA and exponential smoothing to Transformer-based architectures—and introduce a hybrid zero-shot paradigm, where deep models leverage rich embeddings and meta-trained features to predict demand for entirely new products without/with minimal retraining.
Through a real-world retail case study, we demonstrate how zero-shot time series systems reduce stockouts and overstocks, scale effortlessly to thousands of SKUs, and open new avenues for explainable, real-time forecasting. Participants will leave with a clear roadmap for integrating zero-shot techniques into their forecasting pipelines that inform real-world short/long-term inventory decisions

Solo Talk at AI4 2025