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

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

25. Other Books You May Enjoy

References

The following references were provided in this chapter:

Assimakopoulos, Vassilis and Nikolopoulos, K.. (2000). The theta model: A decomposition approach to forecasting. International Journal of Forecasting. 16. 521-530. https://www.researchgate.net/publication/223049702_The_theta_model_A_decomposition_approach_to_forecasting.
Rob J. Hyndman, Baki Billah. (2003). Unmasking the Theta method. International Journal of Forecasting. 19. 287-290. https://robjhyndman.com/papers/Theta.pdf.
Shannon, C.E. (1948), A Mathematical Theory of Communication. Bell System Technical Journal, 27: 379-423. https://people.math.harvard.edu/~ctm/home/text/others/shannon/entropy/entropy.pdf.
Kaboudan, M. (1999). A measure of time series’ predictability using genetic programming applied to stock returns. Journal of Forecasting, 18, 345-357: http://www.aiecon.org/conference/efmaci2004/pdf/GP_Basics_paper.pdf.
Duan, M. (2002). TIME SERIES PREDICTABILITY: https:/...