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Evaluating the efficacy of gradient boosting algorithms in retail demand forecasting: a case study of triumph international
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This thesis is the result of my internship at The Data Cooks, an Amsterdam-based data agency,
and aims to enhance demand forecasting for Triumph International, a leading lingerie
manufacturer. The research is focused on addressing the complex challenge of disaggregated
forecasting in the fashion retail industry, often characterized by a large number of stores and
products. Central to this research is the application of gradient boosting algorithms, a cutting edge approach in machine learning. By focusing on methods such as LightGBM and XGBoost,
this study delves into the effectiveness of ensemble learning in handling complex, time sensitive data.
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Demand forecasting Machine learning Time-series forecasting Fashion retail Master thesis Gradient boosting