You're reading from Modern Time Series Forecasting with Python Explore industry-ready time series forecasting using modern machine learning and deep learning

Product type Paperback

Published in Nov 2022

Publisher Packt

ISBN-13 9781803246802

Length 552 pages

Edition 1st Edition

Languages

Python

Tools

Time Series Forecasting

Concepts

Data Science

Author (1):

Manu Joseph

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Table of Contents (26) Chapters

Preface

1. Part 1 – Getting Familiar with Time Series

2. Chapter 1: Introducing Time Series FREE CHAPTER

3. Chapter 2: Acquiring and Processing Time Series Data

4. Chapter 3: Analyzing and Visualizing Time Series Data

5. Chapter 4: Setting a Strong Baseline Forecast

6. Part 2 – Machine Learning for Time Series

7. Chapter 5: Time Series Forecasting as Regression

8. Chapter 6: Feature Engineering for Time Series Forecasting

9. Chapter 7: Target Transformations for Time Series Forecasting

10. Chapter 8: Forecasting Time Series with Machine Learning Models

11. Chapter 9: Ensembling and Stacking

12. Chapter 10: Global Forecasting Models

13. Part 3 – Deep Learning for Time Series

14. Chapter 11: Introduction to Deep Learning

15. Chapter 12: Building Blocks of Deep Learning for Time Series

16. Chapter 13: Common Modeling Patterns for Time Series

17. Chapter 14: Attention and Transformers for Time Series

18. Chapter 15: Strategies for Global Deep Learning Forecasting Models

19. Chapter 16: Specialized Deep Learning Architectures for Forecasting

20. Part 4 – Mechanics of Forecasting

21. Chapter 17: Multi-Step Forecasting

22. Chapter 18: Evaluating Forecasts – Forecast Metrics

23. Chapter 19: Evaluating Forecasts – Validation Strategies

24. Index

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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 decoder can...

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You're reading from Modern Time Series Forecasting with Python Explore industry-ready time series forecasting using modern machine learning and deep learning

Table of Contents (26) Chapters

Forecasting with sequence-to-sequence models and attention

Authors (1)

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You're reading from Modern Time Series Forecasting with Python Explore industry-ready time series forecasting using modern machine learning and deep learning

Table of Contents (26) Chapters

Forecasting with sequence-to-sequence models and attention

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