8 Result(s)
within
Renyu Zhang
English
Data Mining and Knowledge Discovery
Supply Chain Management
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Chapter
Data Pre-Processing and Modeling Factors
This chapter covers several important pre-processing steps. Before implementing a demand prediction method, it is crucial to process the raw data in order to extract as much predictive power as possible from t...
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Chapter
Tree-Based Methods
This chapter explores tree-based methods for demand prediction. These methods are widely used given their strong predictive power. We consider three types of methods: Decision Tree, Random Forest, and Gradient...
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Chapter
Evaluation and Visualization
In this chapter, we summarize all the results obtained so far and compare the different methods in terms of prediction accuracy. We then present several simple ways to visualize and communicate the prediction ...
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Chapter
Conclusion and Advanced Topics
This chapter aims to conclude this book by first summarizing all the concepts and methods we covered. We then briefly discuss seven more advanced topics related to demand prediction, such as deep learning meth...
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Chapter
Introduction
This chapter introduces the topic of demand prediction for retail applications. We first present several managerial motivations behind demand prediction and discuss the potential practical impact of having goo...
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Chapter
Clustering Techniques
This chapter discusses how to leverage clustering techniques in the context of demand prediction for retail applications. Specifically, our goal is to aggregate the data across different SKUs to improve the pr...
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Chapter
More Advanced Methods
In this chapter, we discuss two more advanced methods, which are recent advancements in demand prediction. We first present the Prophet method, which is an open-sourced library released by Facebook researchers...
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Chapter
Common Demand Prediction Methods
This chapter covers several common methods for demand prediction. We start by presenting the basic linear regression method applied to one SKU. We then explain how to properly structure the dataset. We next di...
