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

Gated recurrent unit (GRU)

In 2014, Cho et al. proposed another variant of the RNN that has a much simpler structure than an LSTM, called a GRU. The intuition behind this is similar to when we use a bunch of gates to regulate the information that flows through the cell, but a GRU eliminates the long-term memory component and uses just the hidden state to propagate information. So instead of the memory cell becoming the gradient highway, the hidden state itself becomes the “gradient highway.” In keeping with the same notation convention we used in the previous section, let’s look at the updated equations for a GRU.

GRU architecture

While we had three gates in an LSTM, we only have two in a GRU:

Reset gate: This gate decides how much of the previous hidden state will be considered as the candidate’s hidden state of the current timestep. The equation for this is:

Update gate: The update gate decides how much of the previous...

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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

Gated recurrent unit (GRU)

GRU architecture

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Authors (2)

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