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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 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

Training a Transformer model with NeuralForecast

Now, we turn our attention to Transformer architectures that have been driving recent advances in various fields of artificial intelligence. In this recipe, we will show you how to train a vanilla Transformer using the NeuralForecast Python library.

Getting ready

Transformers have become a dominant architecture in the deep learning community, especially for natural language processing (NLP) tasks. Transformers have been adopted for various tasks beyond NLP, including time series forecasting.

Unlike traditional models that analyze time series data point by point in sequence, Transformers evaluate all time steps simultaneously. This approach is similar to observing an entire timeline at once, determining the significance of each moment in relation to others for a specific point in time.

At the core of the Transformer architecture is the attention mechanism. This mechanism calculates a weighted sum of input values, or values...

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