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Deep Learning for Time Series Cookbook

You're reading from   Deep Learning for Time Series Cookbook Use PyTorch and Python recipes for forecasting, classification, and anomaly detection

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Product type Paperback
Published in Mar 2024
Publisher Packt
ISBN-13 9781805129233
Length 274 pages
Edition 1st Edition
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Authors (2):
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Luís Roque Luís Roque
Author Profile Icon Luís Roque
Luís Roque
Vitor Cerqueira Vitor Cerqueira
Author Profile Icon Vitor Cerqueira
Vitor Cerqueira
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Toc

Table of Contents (12) Chapters Close

Preface 1. Chapter 1: Getting Started with Time Series 2. Chapter 2: Getting Started with PyTorch FREE CHAPTER 3. Chapter 3: Univariate Time Series Forecasting 4. Chapter 4: Forecasting with PyTorch Lightning 5. Chapter 5: Global Forecasting Models 6. Chapter 6: Advanced Deep Learning Architectures for Time Series Forecasting 7. Chapter 7: Probabilistic Time Series Forecasting 8. Chapter 8: Deep Learning for Time Series Classification 9. Chapter 9: Deep Learning for Time Series Anomaly Detection 10. Index 11. Other Books You May Enjoy

Technical requirements

To work through this chapter, you need to have Python 3.9 installed on your machine. We will work with the following libraries:

  • pandas (2.1.4)
  • numpy (1.26.3)
  • statsmodels (0.14.1)
  • pmdarima (2.0.4)
  • seaborn (0.13.2)

You can install these libraries using pip:

pip install pandas numpy statsmodels pmdarima seaborn

In our setup, we used pip version 23.3.1. The code for this chapter can be found at the following GitHub URL: https://github.com/PacktPublishing/Deep-Learning-for-Time-Series-Data-Cookbook

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