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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 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 Classification

In this chapter, we’ll tackle time series classification (TSC) problems using deep learning. As the name implies, TSC is a classification task involving time series data. The dataset contains several time series, and each of these has an associated categorical label. This problem is similar to a standard classification task, but the input explanatory variables are time series. We’ll explore how to approach this problem using different approaches. Besides using the K-nearest neighbors model to tackle this task, we’ll also develop different neural networks, such as a residual neural network (ResNet) and a convolutional neural network.

By the end of this chapter, you’ll be able to set up a TSC task using a PyTorch Lightning data module and solve it with different models. You’ll also learn how to use the sktime Python library to solve this problem.

This chapter contains the following recipes:

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