Demand Prediction in Retail: A Practical Guide to Leverage Data and Predictive Analytics

Preface
Acknowledgments
Contents
About the Authors
Chapter 1: Introduction
	1.1 Motivation
	1.2 Dataset
	1.3 Objective and Scope
		1.3.1 Training and Test Data
		1.3.2 Prediction Accuracy Metrics
		1.3.3 Application
	References
Chapter 2: Data Pre-Processing and Modeling Factors
	2.1 Dealing with Missing Data
	2.2 Testing for Outliers
	2.3 Accounting for Time Effects
	2.4 Price and Lag-Prices
	2.5 Featured on Main Page
	2.6 Item Descriptive Features
	2.7 Additional Features
	2.8 Scaling
	2.9 Sorting and Exporting the Dataset
	References
Chapter 3: Common Demand Prediction Methods
	3.1 Primer: Basic Linear Regression for One SKU
	3.2 Structuring the Dataset
	3.3 Centralized Approach
	3.4 Decentralized Approach
	3.5 Feature Selection and Regularization
		3.5.1 Subset Selection
			3.5.1.1 Presentation of Subset Selection
			3.5.1.2 AIC and BIC
		3.5.2 Lasso Regularization
			3.5.2.1 Lasso for One SKU
			3.5.2.2 Decentralized Lasso
		3.5.3 Ridge Regularization
			3.5.3.1 Ridge Model
			3.5.3.2 Decentralized Ridge
		3.5.4 Elastic Net Regularization
			3.5.4.1 Elastic Net Model
			3.5.4.2 Decentralized Elastic Net
	3.6 Log Transformations
		3.6.1 Log-Transformation on the Price Variable
		3.6.2 Log-Transformation on the Target Variable
		3.6.3 Transformations and Prediction Accuracy
	3.7 Centralized Approach with SKU-Fixed Effects
	3.8 Centralized Approach with Price-Fixed Effects
	3.9 Centralized Approach with SKU-Price-Fixed Effects
	3.10 Decentralized Approach with Aggregated Seasonality
	3.11 Summary and Next Steps
	References
Chapter 4: Tree-Based Methods
	4.1 Decision Tree
		4.1.1 Centralized Decision Tree
			4.1.1.1 Selecting the Parameters
			4.1.1.2 Focusing on the Best Model
			4.1.1.3 Example of a Plotted Tree
		4.1.2 Decentralized Decision Tree
			4.1.2.1 Selecting the Parameters
			4.1.2.2 Focusing on the Best Model
	4.2 Random Forest
		4.2.1 Centralized Random Forest
			4.2.1.1 Selecting the Parameters
			4.2.1.2 Focusing on the Best Model
		4.2.2 Decentralized Random Forest
			4.2.2.1 Selecting the Parameters
			4.2.2.2 Focusing on the Best Model
	4.3 Gradient-Boosted Tree
		4.3.1 Centralized Gradient Boosted Tree
			4.3.1.1 Selecting the Parameters
			4.3.1.2 Focusing on the Best Model
		4.3.2 Decentralized Gradient-Boosted Tree
			4.3.2.1 Fine-tuning the Parameters
			4.3.2.2 Focusing on the Best Model
	4.4 Methods Comparison
	References
Chapter 5: Clustering Techniques
	5.1 K-means Clustering
		5.1.1 Description of K-means Clustering
		5.1.2 Clustering using Average Price and Weekly Sales
		5.1.3 Adding Standard Deviations of the Clustering Features
	5.2 DBSCAN Clustering
		5.2.1 Description of DBSCAN Clustering
		5.2.2 Clustering using Average Price and Weekly Sales
		5.2.3 Adding the Standard Deviation of the Clustering Features
	References
Chapter 6: Evaluation and Visualization
	6.1 Summary of Results
	6.2 Prediction vs. Actual
	6.3 Varying the Split Ratio
Chapter 7: More Advanced Methods
	7.1 The Prophet Method
		7.1.1 What is the Prophet Method?
			7.1.1.1 How it Works
			7.1.1.2 Illustration for One SKU
		7.1.2 Forecasting with Prophet
			7.1.2.1 Univariate Time-Series
			7.1.2.2 Adding Features
	7.2 Data Aggregation and Demand Prediction
		7.2.1 Presentation of the DAC Method
		7.2.2 Fine-Tuning the Hyperparameters
		7.2.3 Interpretating the DAC Results
	References
Chapter 8: Conclusion and Advanced Topics
	References