You're reading from XGBoost for Regression Predictive Modeling and Time Series Analysis Learn how to build, evaluate, and deploy predictive models with expert guidance

Product type Paperback

Published in Dec 2024

Publisher Packt

ISBN-13 9781805123057

Length 308 pages

Edition 1st Edition

Concepts

Data Governance

Authors (2):

Joyce Weiner

Partha Pritam Deka

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Table of Contents (19) Chapters

Preface

1. Part 1:Introduction to Machine Learning and XGBoost with Case Studies

2. Chapter 1: An Overview of Machine Learning, Classification, and Regression FREE CHAPTER

3. Chapter 2: XGBoost Quick Start Guide with an Iris Data Case Study

4. Chapter 3: Demystifying the XGBoost Paper

5. Chapter 4: Adding on to the Quick Start – Switching out the Dataset with a Housing Data Case Study

6. Part 2: Practical Applications – Data, Features, and Hyperparameters

7. Chapter 5: Classification and Regression Trees, Ensembles, and Deep Learning Models – What’s Best for Your Data?

8. Chapter 6: Data Cleaning, Imbalanced Data, and Other Data Problems

9. Chapter 7: Feature Engineering

10. Chapter 8: Encoding Techniques for Categorical Features

11. Chapter 9: Using XGBoost for Time Series Forecasting

12. Chapter 10: Model Interpretability, Explainability, and Feature Importance with XGBoost

13. Part 3: Model Evaluation Metrics and Putting Your Model into Production

14. Chapter 11: Metrics for Model Evaluations and Comparisons

15. Chapter 12: Managing a Feature Engineering Pipeline in Training and Inference

16. Chapter 13: Deploying Your XGBoost Model

17. Index

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18. Other Books You May Enjoy

Time series data – characteristics and components

There are many uses for data that include a time component, be it minutes, days, months, or seasons. Some uses are for forecasting demand for products that are influenced by seasonality, or for predicting maintenance schedules for manufacturing equipment. Data in the form of measurements taken at intervals indicated by a date or time value is known as time series data. This type of data can present trends where values are rising or falling over time, seasonality where there is a repeating pattern by the time of year, cyclical patterns, or even random unpredictable variation. Consider the following line plot (created by the author) as an example:

Figure 9.1 – A line plot with time on the x-axis (spanning multiple years) and a sample metric (such as sales or temperature) on the y-axis

As demonstrated in the preceding figure, there are four key components to the time series:

Trend: A...

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You're reading from XGBoost for Regression Predictive Modeling and Time Series Analysis Learn how to build, evaluate, and deploy predictive models with expert guidance

Table of Contents (19) Chapters

Time series data – characteristics and components

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Authors (2)

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