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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 FREE CHAPTER 2. Chapter 2: Getting Started with PyTorch 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

Deep Learning for Time Series Anomaly Detection

In this chapter, we’ll delve into anomaly detection problems using time series data. This task involves detecting rare observations that are significantly different from most samples in a dataset. We’ll explore different approaches to tackle this problem, such as prediction-based methods or reconstruction-based methods. This includes using powerful methods such as autoencoders (AEs), variational AEs (VAEs), or generative adversarial networks (GANs).

By the end of this chapter, you’ll be able to define time series anomaly detection problems using different approaches with Python.

The chapter covers the following recipes:

  • Time series anomaly detection with Autoregressive Integrated Moving Average (ARIMA)
  • Prediction-based anomaly detection using deep learning (DL)
  • Anomaly detection using a long short-term memory (LSTM) AE
  • Building an AE using PyOD
  • Creating a VAE for time series anomaly...
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