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

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Key benefits

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

Description

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)

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Amazon verified reviews

Didi Apr 21, 2024

Time series forecasting - making predictions based on historical data - is an important subfield of statistics and machine learning (ML). Following the deep learning (DL) revolution that has completely transformed the fields of computer vision and natural language processing in recent years, the field of time series modeling and analysis is now also being revolutionized by DL-based approaches.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!

Amazon Verified review

Amazon Customer May 06, 2024

"Deep Learning for Time Series Cookbook" by Vitor Cerqueira and Luís Roque is a comprehensive guide for those interested in forecasting, classification, and anomaly detection in time series data. The book caters to readers with a basic knowledge of Python and machine learning, offering practical code snippets to reinforce learning. Each chapter covers essential concepts progressively, from basic time series fundamentals to advanced techniques like N-BEATS and Temporal Fusion Transformers. Topics include univariate and multivariate forecasting, hyperparameter optimization, time series classification using various models, and anomaly detection using autoencoders and generative adversarial networks.Overall, this book is a valuable resource for anyone embarking on their time series modeling journey, providing a blend of theoretical explanations and hands-on examples. It's recommended for readers seeking a practical guide to implementing diverse time series analysis techniques, making it a must-read for those interested in mastering this domain.

Om S May 06, 2024

Ever wondered how to see patterns in everyday numbers and data—like predicting sales spikes at your local store or forecasting weather? This book is like a treasure map that guides you through the mysteries of predicting the future with data. It's called "Deep Learning for Time Series," and it’s all about using a cool tool named PyTorch to make sense of time series data.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.

Amazon Customer May 04, 2024

This book is a comprehensive and practical guide that equips readers with the tools to navigate time series data using advanced deep learning techniques.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.

S May 12, 2024

I've been studying time series forecasting using this book. The concepts are explained very well. The flow is clear and there are numerous examples. It uses several different datasets and applies various approaches to each one. The book starts with simpler, mostly univariate cases. Then, it explains the differences in multivariate cases. It also covers state-of-the-art algorithms and probabilistic settings. Probabilistic forecasting is particularly interesting and I find it extremely useful for understanding the uncertainty of predictions. Great book!

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

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