Build real-world Artificial Intelligence applications with Time Series data and Deep Learning
Take your deep learning skills to the next level by mastering PyTorch with tens of Python recipes
Solve forecasting problems and predict the future using advanced neural network architectures in PyTorch
Key Features
- Learn how to train accurate forecasting model using neural networks and real-world time series
- Build advanced deep neural network architectures using PyTorch
- Tackle several time series tasks, such as forecasting, classification, hierarchical forecasting, and anomaly detection
Book Description
Many real-world systems are captured through the lens of time series. The analysis and forecasting of time series has thus become a key aspect of several organizations. Deep learning is the hottest Artificial Intelligence technology. It leverages large amounts of data to build intricate and accurate forecasting models.
This book is a comprehensive cookbook that guides you through the development of deep learning models for time series data using PyTorch. We start from the basic concepts behind time series analysis and the PyTorch framework. Then, we dive into the details of several time series problems, including forecasting, classification, anomaly detection, and hierarchical time series forecasting. You'll learn how to tackle these tasks with a set of code recipes.
By the end of this book, you'll have a solid understanding of time series data problems and how to tackle them using deep learning based on PyTorch.
What you will learn
- Understand main time series analysis concepts and how to apply them using pandas
- Learn about PyTorch and how to use it to build deep learning models
- Explore how to transform a time series for training transformers and other advanced deep neural networks
- Understand how to deal with various time series characteristics, such as trend, seasonality, or non-constant variance
- Tackle different kinds of forecasting problems, involving univariate, multivariate, or hierarchical time series
- Understand how to apply residual and convolutional neural networks for time series classification problems
- Learn how to solve time series anomaly detection problems using auto-encoders and Generative Adversarial Networks
Who This Book Is For
If you are a machine learning enthusiast or someone who wants to learn more about building forecasting applications using deep learning, this book is for you. In order to learn from this book, you should have basic knowledge of Python and machine learning.
Table of Contents
- Getting Started with Time Series
- Getting Started with keras
- Univariate Time Series Forecasting
- Advanced Forecasting Problems
- 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
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.
