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Deep Learning for Time Series Cookbook: Use PyTorch and Python recipes for forecasting, classification, and anomaly detection
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Learn how to deal with time series data and how to model it using deep learning and take your skills to the next level by mastering PyTorch using different Python recipes
Key Features- Learn the fundamentals of time series analysis and how to model time series data using deep learning
- Explore the world of deep learning with PyTorch and build advanced deep neural networks
- Gain expertise in tackling time series problems, from forecasting future trends to classifying patterns and anomaly detection
- Purchase of the print or Kindle book includes a free PDF eBook
Most organizations exhibit a time-dependent structure in their processes, including fields such as finance. By leveraging time series analysis and forecasting, these organizations can make informed decisions and optimize their performance. Accurate forecasts help reduce uncertainty and enable better planning of operations. Unlike traditional approaches to forecasting, deep learning can process large amounts of data and help derive complex patterns. Despite its increasing relevance, getting the most out of deep learning requires significant technical expertise.
This book guides you through applying deep learning to time series data with the help of easy-to-follow code recipes. You'll cover time series problems, such as forecasting, anomaly detection, and classification. This deep learning book will also show you how to solve these problems using different deep neural network architectures, including convolutional neural networks (CNNs) or transformers. As you progress, you'll use PyTorch, a popular deep learning framework based on Python to build production-ready prediction solutions.
By the end of this book, you'll have learned how to solve different time series tasks with deep learning using the PyTorch ecosystem.
What you will learn- Grasp the core of time series analysis and unleash its power using Python
- Understand PyTorch and how to use it to build deep learning models
- Discover how to transform a time series for training transformers
- Understand how to deal with various time series characteristics
- Tackle forecasting problems, involving univariate or multivariate data
- Master time series classification with residual and convolutional neural networks
- Get up to speed with solving time series anomaly detection problems using autoencoders and generative adversarial networks (GANs)
If you're a machine learning enthusiast or someone who wants to learn more about building forecasting applications using deep learning, this book is for you. Basic knowledge of Python programming and machine learning is required to get the most out of this book.
Table of Contents- Getting Started with Time Series
- Getting Started with PyTorch
- Univariate Time Series Forecasting
- Forecasting with PyTorch Lightning
- Global Forecasting Models
- Advanced Deep Learning Architectures for Time Series Forecasting
- Probabilistic Time Series Forecasting
- Deep Learning for Time Series Classification
- Deep Learning for Time Series Anomaly Detection
- ISBN-101805129236
- ISBN-13978-1805129233
- PublisherPackt Publishing
- Publication dateMarch 29, 2024
- LanguageEnglish
- Dimensions9.25 x 7.52 x 0.57 inches
- Print length274 pages
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Editorial Reviews
About the Author
Vitor Cerqueira is a time series researcher with an extensive background in machine learning. Vitor obtained his Ph.D. degree in Software Engineering from the University of Porto in 2019. He is currently a Post-Doctoral researcher in Dalhousie University, Halifax, developing machine learning methods for time series forecasting. Vitor has co-authored several scientific articles that have been published in multiple high-impact research venues.
Luís Roque, is the Founder and Partner of ZAAI, a company focused on AI product development, consultancy, and investment in AI startups. He also serves as the Vice President of Data & AI at Marley Spoon, leading teams across data science, data analytics, data product, data engineering, machine learning operations, and platforms. In addition, he holds the position of AI Advisor at CableLabs, where he contributes to integrating the broadband industry with AI technologies. Luís is also a Ph.D. Researcher in AI at the University of Porto's AI&CS lab and oversees the Data Science Master's program at Nuclio Digital School in Barcelona. Previously, he co-founded HUUB, where he served as CEO until its acquisition by Maersk.
Product details
- Publisher : Packt Publishing (March 29, 2024)
- Language : English
- Paperback : 274 pages
- ISBN-10 : 1805129236
- ISBN-13 : 978-1805129233
- Item Weight : 1.06 pounds
- Dimensions : 9.25 x 7.52 x 0.57 inches
- Best Sellers Rank: #388,243 in Books (See Top 100 in Books)
- #139 in Data Processing
- #301 in Python Programming
- #462 in Artificial Intelligence & Semantics
- Customer Reviews:
About the authors
Luís Roque is the Founder and Partner of ZAAI, a company focused on AI product development, consultancy, and investment in AI startups. He also serves as the Vice President of Data & AI at Marley Spoon, leading teams across data science, data analytics, data product, data engineering, machine learning operations, and platform. In addition, he holds the position of AI Advisor at CableLabs, where he contributes to integrating the broadband industry with AI technologies.
Luís is also a Ph.D. Researcher in AI at the University of Porto's AI&CS lab and oversees the Data Science Master's program at Nuclio Digital School in Barcelona. He is also a book author, having recently published "Deep Learning for Time Series Cookbook" with Packt. Previously, he co-founded HUUB, where he served as CEO until its acquisition by Maersk.
Vitor is a researcher on machine learning with a focus on time series data. He earned his Ph.D with honours from the University of Porto. He currently holds a research position in FEUP, Faculty of Engineering of the University of Porto
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The book begins by emphasizing the importance of time series analysis in various industries, highlighting how accurate forecasts can drive informed decision-making and optimize organizational performance. By introducing deep learning into the mix, the author showcases how this technology can handle vast amounts of data to uncover intricate patterns, offering a departure from traditional forecasting methods.
One of the standout features of this book is its emphasis on practical application. Through a series of easy-to-follow code recipes, readers are guided through common time series challenges such as forecasting, anomaly detection, and classification. The discussion on utilizing different deep neural network architectures, including CNNs and transformers, adds depth to the exploration of solving time series problems.
Moreover, the book caters to readers of varying skill levels by starting with the basics and gradually progressing to more advanced topics. From understanding the core concepts of time series analysis to mastering PyTorch for building deep learning models, the book ensures a comprehensive learning experience. The chapters on transforming time series data and handling various time series characteristics provide invaluable insights for readers looking to delve deeper into this domain.
By the end of the book, readers are equipped to tackle a range of time series tasks using PyTorch, from univariate and multivariate forecasting to classification and anomaly detection. The inclusion of practical examples and real-world applications adds a layer of relevance to the theoretical concepts discussed throughout the book.
Starting with the basics, the book eases you into the world of PyTorch, a powerful tool used by experts to make predictions. You'll begin with simple single-variable forecasting, which is like looking at one thing at a time, say just the temperature, to predict future trends.
As you get comfortable, the book ramps up to more complex models like PyTorch Lightning and Global Forecasting Models. These chapters are like advanced lessons, helping you handle bigger, worldwide data.
But that's not all. The book also dives into some really brainy stuff—using advanced deep learning architectures and even probabilistic methods for forecasting. It’s like moving from predicting a coin toss to forecasting stock market trends!
Then, there's the fascinating world of classifying time series data and detecting anomalies. Imagine being able to spot when something unusual happens—like detecting fraudulent transactions just by looking at patterns.
For anyone curious about stepping into the future of forecasting or enhancing their skills in Python and deep learning, this book acts as a solid stepping stone. And don’t worry, it's written in a way that even beginners with a bit of Python knowledge can follow along.
This book is a unique and comprehensive guide to time series forecasting, classification, and analysis using DL. This practical guide begins with an introduction to time series modeling using Python, including topics such as time series visualization, resampling, and dealing with missing data. It proceeds with an introduction to the PyTorch and PyTorch Lightning libraries and their use for time series forecasting, followed by a description of advanced DL architectures and methods for forecasting, such as the use of transformers and probabilistic forecasting. The last part of the book describes a variety of methods for solving the important problems of time series classification and anomaly detection.
To get the most out of this book, readers are expected to have some familiarity with Python, and preferably also with its popular data manipulation libraries such as pandas and NumPy. The accompanying GitHub repo is well-organized and very helpful in reinforcing the concepts described in the book.
This book is a wonderful, up-to-date resource for researchers, data scientists, and software engineers interested in building DL-based time series forecasting and analysis models in Python. Highly recommended!