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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

Who this book is for

This book is primarily designed for beginners to data science, and those who are eager to delve into the application of deep learning for time series analysis and forecasting. We assume that you have a basic understanding of Python, which will help you to navigate the coding recipes more easily. We also rely on popular data manipulation libraries such as pandas and NumPy. So, a familiarity with these will improve your reading experience.

We expect you to have basic knowledge concerning fundamental machine learning concepts and techniques. Understanding things such as supervised and unsupervised learning, as well as being familiar with classification, regression, cross-validation, and evaluation methodologies, is important to get the most out of this book.

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