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

TSMixer

While the Transformer-based models were forging ahead with steam, a parallel track of research started by using Multi-Layer Perceptrons (MLPs) instead of Transformers as the key learning unit. The trend kicked off in 2021 when MLP-Mixer showed that one can attain state-of-the-art performance in vision problems by using just MLPs, replacing Convolutional Neural Networks. And so, similar mixer architectures using MLPs as the key learning component started popping up in all domains. In 2023, Si-An Chen et al. from Google brought mixing MLPs into time series forecasting.

Reference check:

The research paper by Si-An et al. on TSMixer is cited in the References section as 19.

The architecture of the TSMixer model

TSMixer really took inspiration from the Transformer model but tried to replicate similar processes with an MLP. Let’s use Figure 16.10 to understand the similarities and differences.

Figure 16.12: Transformer vs TSMixer

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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

TSMixer

The architecture of the TSMixer model

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Authors (2)

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