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 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

Getting Started with Time Series

In this chapter, we introduce the main concepts and techniques used in time series analysis. The chapter begins by defining time series and explaining why the analysis of these datasets is a relevant topic in data science. After that, we describe how to load time series data using the pandas library. The chapter dives into the basic components of a time series, such as trend and seasonality. One key concept of time series analysis covered in this chapter is that of stationarity. We will explore several methods to assess stationarity using statistical tests.

The following recipes will be covered in this chapter:

  • Loading a time series using pandas
  • Visualizing a time series
  • Resampling a time series
  • Dealing with missing values
  • Decomposing a time series
  • Computing autocorrelation
  • Detecting stationarity
  • Dealing with heteroskedasticity
  • Loading and visualizing a multivariate time series
  • Resampling a multivariate time series
  • Analyzing the correlation among pairs of variables

By the end of this chapter, you will have a solid foundation in the main aspects of time series analysis. This includes loading and preprocessing time series data, identifying its basic components, decomposing time series, detecting stationarity, and expanding this understanding to a multivariate setting. This knowledge will serve as a building block for the subsequent chapters.

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