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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

Technical requirements

Before diving into univariate time series forecasting problems, we need to ensure that we have the appropriate software and libraries installed on our system. Here, we’ll go over the main technical requirements for implementing the procedures described in this chapter:

  • We will primarily need Python 3.9 or a later version, pip or Anaconda, PyTorch, and CUDA (optional). You can check the Installing PyTorch recipe from the previous chapter for more information on these.
  • NumPy (1.26.3) and pandas (2.1.4): Both these Python libraries provide several methods for data manipulation and analysis.
  • statsmodels (0.14.1): This library implements several statistical methods, including a few useful time series analysis techniques.
  • scikit-learn (1.4.0): scikit-learn is a popular Python library for statistical learning. It contains several methods to solve different tasks, such as classification, regression, and clustering.
  • sktime (0.26.0): A Python...
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