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 are the references used in this chapter:

Tashman, Len. (2000). Out-of-sample tests of forecasting accuracy: An analysis and review. International Journal of Forecasting. 16. 437-450. 10.1016/S0169-2070(00)00065-0: https://www.researchgate.net/publication/223319987_Out-of-sample_tests_of_forecasting_accuracy_An_analysis_and_review
Bergmeir, Christoph and Benítez, José M. (2012). On the use of cross-validation for time series predictor evaluation. In Information Sciences, Volume 191, 2012, Pages 192-213: https://www.sciencedirect.com/science/article/abs/pii/S0020025511006773
Cerqueira, V., Torgo, L., and Mozetič, I. (2020). Evaluating time series forecasting models: an empirical study on performance estimation methods. Mach Learn 109, 1997–2028 (2020): https://doi.org/10.1007/s10994-020-05910-7
Snijders, T.A.B. (1988). On Cross-Validation for Predictor Evaluation in Time Series. In: Dijkstra, T.K. (eds) On Model Uncertainty...