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 is the list of the references that we used throughout the chapter:

Taieb, S.B., Bontempi, G., Atiya, A.F., and Sorjamaa, A. (2012). A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition. Expert Syst. Appl., 39, 7067-7083: https://arxiv.org/pdf/1108.3259.pdf
Li Zhang, Wei-Da Zhou, Pei-Chann Chang, Ji-Wen Yang, Fan-Zhang Li. (2013). Iterated time series prediction with multiple support vector regression models. Neurocomputing, Volume 99, 2013: https://www.sciencedirect.com/science/article/pii/S0925231212005863
Taieb, S.B. and Atiya, A.F. (2016). A Bias and Variance Analysis for Multistep-Ahead Time Series Forecasting. in IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 1, pp. 62-76, Jan. 2016: https://ieeexplore.ieee.org/document/7064712