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

Forecasting with Transformers

For some continuity, we will use the same household example we were forecasting with RNNs and RNNs with attention.

Notebook alert:

To follow along with the complete code, use the notebook named 03-Transformers.ipynb in the Chapter14 folder and the code in the src folder.

Although we learned about the vanilla Transformer as a model with an encoder-decoder architecture, it was really designed for language translation tasks. In language translation, the source sequence and target sequence are quite different, and therefore the encoder-decoder architecture made sense. But soon after, researchers figured out that using the decoder part of the Transformer alone does well. It is called a decoder-only Transformer in literature. The naming is a bit confusing because if you think about it, the decoder is different from the encoder in two ways—masked self-attention and encoder-decoder attention. So, in a decoder-only Transformer...

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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

Forecasting with Transformers

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Authors (2)

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