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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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Toc

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

Temporal Fusion Transformer (TFT)

TFT is a model that is thoughtfully designed from the ground up to make the most efficient use of all the different kinds of information in a global modeling context – static and dynamic variables. TFT also has interpretability at the heart of all design decisions. The result is a high-performing, interpretable, and global DL model.

Reference check

The research paper by Lim et al. on TFT is cited in the References section as 10.

At first glance, the model architecture looks complicated and daunting. But once you peel the onion, it is quite simple and ingenious. We will take this one level of abstraction at a time to ease you into the full model. Along the way, there will be many black boxes I’m going to ask you to take for granted, but don’t worry – we will open every one of them as we dive deeper.

The Architecture of TFT

Let’s establish some notations and a setting before we start. We have a dataset...

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