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

Patch Time Series Transformer (PatchTST)

In 2021, Alexey Dosovitskiy et al. proposed Vision Transformer, which introduced the Transformer architecture which was widely successful in Natural Language Processing to Vision. Although not the first to introduce patching, they applied it in a way that works really well for vision. The design broke up an image into patches and fed the transformer each patch in sequence.

Reference check:

The research paper by Alexey Dosovitskiy et al. on Vision Tranformers and Yuqi Nie et al. on PatchTST are cited in the References section as 12 and 13, respectively.

Fast-forward to 2023, and we have the same patching design applied to time series forecasting. Yuqi Nie et al. proposed Patch Time Series Transformer (PatchTST) by adopting the patching design for time series. They were motivated by the apparent ineffectiveness of more complicated Transformer designs (like Autoformer and Informer) on time series forecasting.

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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

Patch Time Series Transformer (PatchTST)

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Authors (2)

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