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 sequence-to-sequence models and attention

Let’s pick up the thread from Chapter 13, Common Modeling Patterns for Time Series, where we used Seq2Seq models to forecast a sample household (if you have not read the previous chapter, I strongly suggest you do it now) and modify the Seq2SeqModel class to also include an attention mechanism.

Notebook alert:

To follow along with the complete code, use the notebook named 01-Seq2Seq_RNN_with_Attention.ipynb in the Chapter14 folder and the code in the src folder.

We are still going to inherit the BaseModel class we have defined in src/dl/models.py, and the overall structure is going to be very similar to the Seq2SeqModel class. The key difference will be that in our new model, with attention, we do not accept a fully connected layer as the decoder. It is not because it is not possible, but for convenience and brevity of the implementation. In fact, implementing a Seq2Seq model with a fully connected...

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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 sequence-to-sequence models and attention

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Authors (2)

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