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Modern Time Series Forecasting with Python

You're reading from Modern Time Series Forecasting with Python Explore industry-ready time series forecasting using modern machine learning and deep learning

Product type Paperback

Published in Nov 2022

Publisher Packt

ISBN-13 9781803246802

Length 552 pages

Edition 1st Edition

Languages

Python

Tools

Time Series Forecasting

Concepts

Data Science

Author (1):

Manu Joseph

View More author details

Table of Contents (26) Chapters

Preface

1. Part 1 – Getting Familiar with Time Series

2. Chapter 1: Introducing Time Series FREE CHAPTER

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

Why subscribe?

25. Other Books You May Enjoy

Further reading

To learn more about the topics that were covered in this chapter, take a look at the following resources:

A Kaggler’s Guide to Model Stacking in Practice, by Ha Nguyen: https://datasciblog.github.io/2016/12/27/a-kagglers-guide-to-model-stacking-in-practice/
Kai Ming Ting and Ian H. Witten (1997), Stacked Generalization: when does it work?: https://www.ijcai.org/Proceedings/97-2/Papers/011.pdf
Pablo Montero-Manso, George Athanasopoulos, Rob J. Hyndman, Thiyanga S. Talagala (2020), FFORMA: Feature-based forecast model averaging. International Journal of Forecasting, Volume 36, Issue 1: https://robjhyndman.com/papers/fforma.pdf
Peiyi Zhang, et al. (2021), Self-supervised learning for fast and scalable time-series hyper-parameter tuning: https://www.ijcai.org/Proceedings/97-2/Papers/011.pdf
Local versus Global Optima: https://www.mathworks.com/help/optim/ug/local-vs-global-optima.html

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Authors (1)

Manu Joseph

Manu Joseph

Manu Joseph is a self-made data scientist with more than a decade of experience working with many Fortune 500 companies enabling digital and AI transformations, specifically in machine learning-based demand forecasting. He is considered an expert, thought leader, and strong voice in the world of time series forecasting. Currently, Manu leads applied research at Thoucentric, where he advances research by bringing cutting-edge AI technologies to the industry. He is also an active open-source contributor and developed an open-source library—PyTorch Tabular—which makes deep learning for tabular data easy and accessible. Originally from Thiruvananthapuram, India, Manu currently resides in Bengaluru, India, with his wife and son

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