Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases now! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in Mar 2024
Publisher Packt
ISBN-13 9781805129233
Length 274 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Authors (2):
Arrow left icon
Luís Roque Luís Roque
Author Profile Icon Luís Roque
Luís Roque
Vitor Cerqueira Vitor Cerqueira
Author Profile Icon Vitor Cerqueira
Vitor Cerqueira
Arrow right icon
View More author details
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

Forecasting with PyTorch Lightning

In this chapter, we’ll build forecasting models using PyTorch Lightning. We’ll touch on several aspects of this framework, such as creating a data module to handle data preprocessing or creating a LightningModel structure that encapsulates the training process of neural networks. We’ll also explore TensorBoard to monitor the training process of neural networks. Then, we’ll describe a few metrics for evaluating deep neural networks for forecasting, such as Mean Absolute Scaled Error (MASE) and Symmetric Mean Absolute Percentage Error (SMAPE). In this chapter, we’ll focus on multivariate time series, which contain more than one variable.

This chapter will guide you through the following recipes:

  • Preparing a multivariate time series for supervised learning
  • Training a linear regression model for forecasting with a multivariate time series
  • Feedforward neural networks for multivariate time series forecasting...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime