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 a list of the references that we used throughout this chapter:

Spyros Makridakis, Evangelos Spiliotis, and Vassilios Assimakopoulos. (2020). The M4 Competition: 100,000 time series and 61 forecasting methods. International Journal of Forecasting, Volume 36, Issue 1. Pages 54-74. https://doi.org/10.1016/j.ijforecast.2019.04.014.
Slawek Smyl. (2018). M4 Forecasting Competition: Introducing a New Hybrid ES-RNN Model. https://www.uber.com/blog/m4-forecasting-competition/.
Boris N. Oreshkin, Dmitri Carpov, Nicolas Chapados, and Yoshua Bengio. (2020). N-BEATS: Neural basis expansion analysis for interpretable time series forecasting. 8th International Conference on Learning Representations, (ICLR). https://openreview.net/forum?id=r1ecqn4YwB.
Kin G. Olivares and Cristian Challu and Grzegorz Marcjasz and R. Weron and A. Dubrawski. (2022). Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx. International...