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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
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Vitor Cerqueira
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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

We’ll focus on the PyTorch Lightning ecosystem to build deep learning models. Besides that, we’ll also use scikit-learn to create a baseline. Overall, the list of libraries used in the package is the following:

  • scikit-learn (1.3.2)
  • pandas (2.1.3)
  • NumPy (1.26.2)
  • Torch (2.1.1)
  • PyTorch Lightning (2.1.2)
  • sktime (0.24.1)
  • keras-self-attention (0.51.0)

As an example, we’ll use the Car dataset from the repository available at the following link: https://www.timeseriesclassification.com. You can learn more about the dataset in the following work:

Thakoor, Ninad, and Jean Gao. Shape classifier based on generalized probabilistic descent method with hidden Markov descriptor. Tenth IEEE International Conference on Computer Vision (ICCV’05) Volume 1. Vol. 1. IEEE, 2005.

The code and datasets used in this chapter can be found at the following GitHub URL: https://github.com/PacktPublishing/Deep-Learning...

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