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- Explore industry-tested machine learning techniques used to forecast millions of time series
- Get started with the revolutionary paradigm of global forecasting models
- Get to grips with new concepts by applying them to real-world datasets of energy forecasting

We live in a serendipitous era where the explosion in the quantum of data collected and a renewed interest in data-driven techniques such as machine learning (ML), has changed the landscape of analytics, and with it, time series forecasting. This book, filled with industry-tested tips and tricks, takes you beyond commonly used classical statistical methods such as ARIMA and introduces to you the latest techniques from the world of ML.
This is a comprehensive guide to analyzing, visualizing, and creating state-of-the-art forecasting systems, complete with common topics such as ML and deep learning (DL) as well as rarely touched-upon topics such as global forecasting models, cross-validation strategies, and forecast metrics. You’ll begin by exploring the basics of data handling, data visualization, and classical statistical methods before moving on to ML and DL models for time series forecasting. This book takes you on a hands-on journey in which you’ll develop state-of-the-art ML (linear regression to gradient-boosted trees) and DL (feed-forward neural networks, LSTMs, and transformers) models on a real-world dataset along with exploring practical topics such as interpretability.
By the end of this book, you’ll be able to build world-class time series forecasting systems and tackle problems in the real world.

Find out how to manipulate and visualize time series data like a pro
Set strong baselines with popular models such as ARIMA
Discover how time series forecasting can be cast as regression
Engineer features for machine learning models for forecasting
Explore the exciting world of ensembling and stacking models
Get to grips with the global forecasting paradigm
Understand and apply state-of-the-art DL models such as N-BEATS and Autoformer
Explore multi-step forecasting and cross-validation strategies

Download this book in **EPUB** and **PDF** formats

Access this title in our online reader with advanced features

Publication date :
Nov 24, 2022

Length
552 pages

Edition :
1st Edition

Language :
English

ISBN-13 :
9781803246802

Category :

Concepts :

Preface

1. Part 1 – Getting Familiar with Time Series

2. Chapter 1: Introducing Time Series

3. Chapter 2: Acquiring and Processing Time Series Data

4. Chapter 3: Analyzing and Visualizing Time Series Data

5. Chapter 4: Setting a Strong Baseline Forecast

6. Part 2 – Machine Learning for Time Series

7. Chapter 5: Time Series Forecasting as Regression

8. Chapter 6: Feature Engineering for Time Series Forecasting

9. Chapter 7: Target Transformations for Time Series Forecasting

10. Chapter 8: Forecasting Time Series with Machine Learning Models

11. Chapter 9: Ensembling and Stacking

12. Chapter 10: Global Forecasting Models

13. Part 3 – Deep Learning for Time Series

14. Chapter 11: Introduction to Deep Learning

15. Chapter 12: Building Blocks of Deep Learning for Time Series

16. Chapter 13: Common Modeling Patterns for Time Series

17. Chapter 14: Attention and Transformers for Time Series

18. Chapter 15: Strategies for Global Deep Learning Forecasting Models

19. Chapter 16: Specialized Deep Learning Architectures for Forecasting

20. Part 4 – Mechanics of Forecasting

21. Chapter 17: Multi-Step Forecasting

22. Chapter 18: Evaluating Forecasts – Forecast Metrics

23. Chapter 19: Evaluating Forecasts – Validation Strategies

24. Index

25. Other Books You May Enjoy

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Machiel Kruger
Feb 22, 2024

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