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

View More author details

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

Using time-varying information

The GFM(ML) used all the available features. So, obviously, that model had access to a lot more information than the GFM(DL) we have built until now. The GFM(DL) we just built only takes in the history and nothing else. Let’s change that by including time-varying information. We will just use time-varying real features this time because dealing with categorical features is a topic I want to leave for the next section.

We initialize the training dataset the same way as before, but we add time_varying_known_reals=feat_config.time_varying_known_reals to the initialization parameters. Now that we have all the datasets created, let’s move on to setting up the model.

To set up the model, we need to understand one concept. We are now using the history of the target and time-varying known features. In Figure 15.3, we saw how TimeSeriesDataset arranges the different kinds of variables in PyTorch tensors. In the previous section, we used...

The rest of the chapter is locked

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

Using time-varying information

Unlock this book and the full library FREE for 7 days

Authors (2)

Personalised recommendations for you