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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
Languages
Tools
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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 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

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

A

anomaly detection

with long short-term memory (LSTM) AE 225-232

Anomaly Detection with Generative Adversarial Networks (AnoGAN) 239

Asynchronous Successive Halving Algorithm (ASHA) 131

attention mechanism 149

autocorrelation

computing 14-16

autocorrelation function (ACF) 48

autoencoders (AEs) 217

building, with PyOD 232-236

Auto-Regressive Distributed Lags (ARDL) 88

Autoregressive Integrated Moving Average (ARIMA) 47

components 48

time series anomaly detection with 218-220

univariate forecasting with 47, 48

B

Box-Cox transformation 80

modeling 81, 82

C

conditional GANs (CGANs) 242

conformal prediction

used, for creating prediction intervals 173-176

Convolutional Neural Networks (CNNs) 64

for TSC 202- 205

training 40, 41

...
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