Titlebook: Demand Prediction in Retail; A Practical Guide to Maxime C. Cohen,Paul-Emile Gras,Renyu Zhang Textbook 2022 The Editor(s) (if applicable) a

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聲明

津貼

Common Demand Prediction Methods,n explain how to properly structure the dataset. We next discuss two approaches in terms of data aggregation: the centralized approach (which combines the data across all SKUs and estimate a single model) and the decentralized approach (which estimates a different model for each SKU by solely relyin

暖昧關(guān)系

Tree-Based Methods,ypes of methods: Decision Tree, Random Forest, and Gradient Boosted Tree. We apply these methods under both the centralized and decentralized approaches. For each method, we briefly discuss the underlying mathematical framework, present a common practical way to select the parameters, and detail the

sulcus

Clustering Techniques, aggregate the data across different SKUs to improve the prediction accuracy. On the one hand, aggregating sales data across several SKUs will help reduce the noise and would allow the model to rely on a larger number of observations. On the other hand, this will overlook the fact that each SKU bear

Dedication

More Advanced Methods,is an open-sourced library released by Facebook researchers in 2017. Prophet is a time-series demand prediction method that often performs well on large-scale problems. We explain the method and discuss its implementation both with and without incorporating features. We then present a method that ca

Dedication

Conclusion and Advanced Topics,cs related to demand prediction, such as deep learning methods, transfer learning, and data censoring. For each topic, we provide a number of relevant references for interested readers. We close by discussing several decisions that can be guided by prescriptive analytics tools that rely on demand pr

reflection

深陷

夾死提手勢(shì)