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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Sequence-to-sequence (Seq2Seq) models

We talked in detail about the sequence-to-sequence (Seq2Seq) architecture and the encoder-decoder paradigm in Chapter 12, Building Blocks of Deep Learning for Time Series. Just to refresh your memory, the Seq2Seq model is a kind of an encoder-decoder model by which an encoder encodes the sequence into a latent representation, and then the decoder steps in to carry out the task at hand using this latent representation. This setup is inherently more flexible because of the separation between the encoder (which does the representation learning) and the decoder, which uses the representation for predictions. One of the biggest advantages of this approach, from a time series forecasting perspective, is that the restriction of single step ahead is taken out. Under this modeling pattern, we can extend the forecast to any forecast horizon we want.

In this section, let’s put together a few encoder-decoder models and test out our single-step-ahead...