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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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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

Tackling TSC problems with sktime

In this recipe, we explore an alternative approach to PyTorch for TSC problems, which is sktime. sktime is a Python library devoted to time series modeling, which includes several neural network models for TSC.

Getting ready

You can install sktime using pip. You’ll also need the keras-self-attention library, which includes self-attention methods necessary for running some of the methods in sktime:

pip install 'sktime[dl]'
pip install keras-self-attention

The trailing dl tag in squared brackets when installing sktime means you want to include the optional deep learning models available in the library.

In this recipe, we’ll use an example dataset available in sktime. We’ll load it in the next section.

How to do it…

As the name implies, the sktime library follows a design pattern similar to scikit-learn. So, our approach to building a deep learning model using sktime will be similar to the workflow...

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