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

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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

Understanding the basics of machine learning

We want to use machine learning for time series forecasting. But before we get started with it, let’s spend some time establishing what machine learning is and setting up a framework to demonstrate what it does (if you are already very comfortable with machine learning, feel free to skip ahead to the next section, Time series forecasting as regression, or just stay with us and refresh the concepts). In 1959, Arthur Samuel defined machine learning as a “field of study that gives computers the ability to learn without being explicitly programmed.” Traditionally, programming has been a paradigm under which we know a set of rules/logic to perform an action, and that action is performed on the given data to get the output that we want. But machine learning flipped this on its head.

In machine learning, we start with data and the output, and we ask the computer to tell us about the rules with which the desired output...

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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

Understanding the basics of machine learning

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Authors (2)

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