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

Following is the list of the reference used throughout this chapter:

Kyoung-Su Oh and Keechul Jung. (2004), GPU implementation of neural networks. Pattern Recognition, Volume 37, Issue 6, 2004: https://doi.org/10.1016/j.patcog.2004.01.013.
Rajat Raina, Anand Madhavan, and Andrew Y. Ng. (2009), Large-scale deep unsupervised learning using graphics processors. In Proceedings of the 26th Annual International Conference on Machine Learning (ICML ‘09): https://doi.org/10.1145/1553374.1553486.
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. (2012), ImageNet Classification with Deep Convolutional Neural Networks. Commun. ACM 60, 6 (June 2017), 84–90: https://doi.org/10.1145/3065386.
Neil C. Thompson, Kristjan Greenewald, Keeheon Lee, and Gabriel F. Manso. (2020). The Computational Limits of Deep Learning. arXiv:2007.05558v1 [cs.LG]: https://arxiv.org/abs/2007.05558v1.
Frank Rosenblatt. (1957), The perceptron – A perceiving and...