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

Introducing Time Series

Welcome to Modern Time Series Forecasting with Python! This book is intended for data scientists or machine learning (ML) engineers who want to level up their time series analysis skills by learning new and advanced techniques from the ML world. Time series analysis is something that is commonly overlooked in regular ML books, courses, and so on. They typically start with classification, touch upon regression, and then move on. But it is also something that is immensely valuable and ubiquitous in business. We look at the world from a three-dimensional perspective. Time is the hidden dimension that we rarely think about, but is all-pervasive. And as long as time is one of the four dimensions in the world we live in, time series data is all-pervasive too.

Analyzing time series data unlocks a lot of value for a business. Time series analysis isn’t new—it’s been around since the 1920s. But in the current age of data, the time series that...

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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

Introducing Time Series

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Authors (2)

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