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

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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 gated recurrent unit (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.

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: