Deep Learning for Time Series Cookbook: Use PyTorch and Python recipes for forecasting, classification, and anomaly detection

by Vitor Cerqueira, Luís Roque

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 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. 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.

274 pages, Paperback

Published March 29, 2024