Forecasting demand for new products presents a significant challenge for businesses in retail, e-commerce, and logistics, as traditional time-series methods often fail due to the lack of historical sales data. A recent approach utilizing transfer learning with gradient boosting models improved forecasting accuracy by leveraging data from existing products to inform predictions for new ones, reducing the weighted absolute percentage error (WAPE) from 70% to 36%. This technique highlights the value of adapting established models to address unique data limitations and offers a practical solution for businesses launching new products.
Read the full article at Towards AI - Medium
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