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

Hybrid strategies

The three strategies we have already covered are the three basic strategies for multi-step forecasting, each with its own merits and demerits. Over the years, researchers have tried to combine these as hybrid strategies that try to capture the good parts of each strategy. Let’s go through a few of them here. This is not a comprehensive list because there is none. Anyone with enough creativity can come up with alternate strategies, but we will just cover a few that have received some attention and deep study from the forecasting community.

DirRec strategy

As the name suggests, the DirRec strategy is a combination of direct and recursive strategies for multi-step forecasting. One of the disadvantages of the direct method is that it forecasts each timestep independently and, therefore, loses out on some context when predicting far into the future. To rectify this shortcoming, we combine the direct and recursive methods by using the forecast generated...

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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

Hybrid strategies

DirRec strategy

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Authors (2)

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