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Modern Time Series Forecasting with Python

You're reading from   Modern Time Series Forecasting with Python Explore industry-ready time series forecasting using modern machine learning and deep learning

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Product type Paperback
Published in Nov 2022
Publisher Packt
ISBN-13 9781803246802
Length 552 pages
Edition 1st Edition
Languages
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Author (1):
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Manu Joseph Manu Joseph
Author Profile Icon Manu Joseph
Manu Joseph
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Table of Contents (26) Chapters Close

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 25. Other Books You May Enjoy

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