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

View More author details

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

Why subscribe?

25. Other Books You May Enjoy

Single-step-ahead recurrent neural networks

Although we took a little detour to check out how DL regression models can be used to train the same global models we learned about in Chapter 10, Global Forecasting Models, now we are back to looking at DL models and architectures specifically built for time series. And as always, we will look at simple one-step-ahead and local models first before moving on to more complex modeling paradigms. In fact, we have another chapter (Chapter 15, Strategies for Global Deep Learning Forecasting Models) entirely devoted to techniques we can use to train global DL models.

Now, let’s bring our attention back to one-step-ahead local models. We saw recurrent neural networks (RNNs) (vanilla RNN, long short-term memory (LSTM), and gated recurrent unit (GRU)) as a few blocks we can use for sequences such as time series. Now, let’s see how we can use them in an end-to-end (E2E) model on the dataset we have been working on (the London smart...