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

LTSF-Linear family of models

There has been a lot of debate on whether Transformers are right for forecasting problems, how popular Transformer papers haven’t used strong baselines to show their superiority, how the order-agnostic attention mechanism may not be the best way to approach strongly ordered time series, and so on. The criticism was more pronounced for Long-Term Time Series Forecasting as it relies more on the extraction of strong trends and seasonalities. In 2023, Ailing Zeng et al. decided to put the Transformer models to the test and conducted a wide study using 5 multivariate datasets, pitting five Transformer models (FEDFormer, Autoformer, Informer, Pyraformer, and LogTrans) against a set of simple linear models that they proposed. Surprisingly, the simple linear models they proposed beat all the Transformer models comfortably.

Reference check:

The research papers by Ailing Zeng et al. and the different Transformer models, FEDFormer, Autoformer...

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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

LTSF-Linear family of models

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Authors (2)

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