You're reading from Modern Time Series Forecasting with Python Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas

Product type Paperback

Published in Oct 2024

Publisher Packt

ISBN-13 9781835883181

Length 658 pages

Edition 2nd Edition

Languages

Python

Tools

PyTorch

Concepts

Data Science

Authors (2):

Jeffrey Tackes

Manu Joseph

View More author details

Table of Contents (26) Chapters

Preface

1. Part-1: Getting Familiar with Time Series FREE CHAPTER

2. Introducing Time Series

3. Acquiring and Processing Time Series Data

4. Analyzing and Visualizing Time Series Data

5. Setting a Strong Baseline Forecast

6. Part-2: Machine Learning for Time Series

7. Time Series Forecasting as Regression

8. Feature Engineering for Time Series Forecasting

9. Target Transformations for Time Series Forecasting

10. Forecasting Time Series with Machine Learning Models

11. Ensembling and Stacking

12. Global Forecasting Models

13. Part-3: Deep Learning for Time Series

14. Introduction to Deep Learning

15. Building Blocks of Deep Learning for Time Series

16. Common Modeling Patterns for Time Series

17. Attention and Transformers for Time Series

18. Strategies for Global Deep Learning Forecasting Models

19. Specialized Deep Learning Architectures for Forecasting

20. Probabilistic Forecasting and More

21. Part-4: Mechanics of Forecasting

22. Multi-Step Forecasting

23. Evaluating Forecast Errors—A Survey of Forecast Metrics

24. Evaluating Forecasts—Validation Strategies

25. Index

Deep Learning for Time Series

In this part, we focus on the exciting field of deep learning to tackle time series problems. This part starts with a good introduction of the necessary concepts and slowly builds up to different specialized architectures that are suited to handle time series data. It also talks about global models in deep learning and some strategies to make them work better. And to top it off, we dive deep into generating probabilistic forecasts which is highly relevant in today’s forecasting landscape.

This part comprises the following chapters:

Chapter 11, Introduction to Deep Learning
Chapter 12, Building Blocks of Deep Learning for Time Series
Chapter 13, Common Modeling Patterns for Time Series
Chapter 14, Attention and Transformers for Time Series
Chapter 15, Strategies for Global Deep Learning Forecasting Models
Chapter 16, Specialized Deep Learning Architectures for Forecasting
Chapter 17, Probabilistic...

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You're reading from Modern Time Series Forecasting with Python Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas

Table of Contents (26) Chapters

Deep Learning for Time Series

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Authors (2)

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