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Time Series with PyTorch : Modern Deep Learning Toolkit for Real-World Forecasting Challenges

Davidson, Graeme/ Ma, Lei
95.20 SGD
会員価格
85.68
English

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Product Description

Time series is far more than fit-predict forecasting. Real mastery comes from intuition and is built through experimentation. Walk the full range with two practitioners: forecasting, conformal prediction, transfer learning, and beyond. Key Features Grasp core concepts through clear explanations that build genuine understanding rather than surface familiarity Work with realistic datasets and develop the judgement to choose the right approach for your problem Progress from neural network fundamentals to advanced techniques across a full range of time series challenges. Book DescriptionNeural networks are powerful tools for time-series forecasting, but applying them effectively requires both practical experience and a clear understanding of architectures, training strategies, and evaluation methods. This book brings these ideas together in a structured and practical way. Starting with PyTorch fundamentals, you will build neural networks from scratch and progress through recurrent networks, attention mechanisms, and transformers before exploring forecasting architectures such as N-BEATS, N-HiTS, and the Temporal Fusion Transformer. Along the way, you will learn robust hyperparameter tuning, conformal prediction for uncertainty estimation, and reliable evaluation practices. Unlike most forecasting books, this text also explores topics often overlooked or treated separately, including transfer learning across collections of series, synthetic data generation with diffusion models, and self-supervised representation learning. Beyond forecasting, later chapters cover classification, clustering, anomaly detection, and embeddings for large-scale time-series modeling. Throughout, the focus is pragmatic: theory is reinforced through experimentation and implementation so you can apply these methods confidently to real-world time-series problems.What you will learn Build, train, and evaluate neural networks for time series using PyTorch and PyTorch Lightning. Tune models with Baye
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Leverage time series analysis for better decision making with cutting-edge tools and techniques

Key Features

Get to grips with concepts through jargon-busting explanations
Learn to use a variety of datasets that reflect problems you're likely to encounter in everyday practice
Understand how to select the appropriate algorithms to avoid unnecessary complexity
Learn from progressive and pedagogical chapters that guides you from introductory toy problems to end-to-end real-world projects

Book DescriptionDeep learning (DL) is a cutting-edge approach to learning from data. While it has taken the areas of computer vision and natural language processing by storm, its application to time-series forecasting is a more recent phenomenon and remains challenging for both new and experienced practitioners.
To develop the best time series models for a real-world problem, it is essential to have not only a thorough understanding of the time series data but also a solid grasp of DL models themselves. This book investigates time series structures and the DL approaches that can address the variety of challenges they present to practitioners in industry.
In this book, you will gain insights from a variety of perspectives, both from the data and the models. You will learn about the complexities of real-world time series data, explore the different problem settings for time series analysis, touch upon the foundation of DL models for time series, and practice end-to-end time series analysis projects when DL works; the authors believe in choosing the best tool for the problem, so traditional methods are never far from our minds. A GitHub repository with coding examples will be provided to support your journey.
By the end of this book, you will be able to approach almost any time series challenge with an appropriate model that gets you results.What you will learn

Develop an understanding of how to code and test neural networks with PyTorch and PyTorch Lightning
Address challenges presented by different data structures with neural architecture
Learn advanced methods to evaluate and validate models by comparing and optimizing them and partitioning your data correctly
Gain insight into how time series models work behind the scenes and why a model fits a particular type of problem
Apply contemporary approaches like TFT, NBEATs, and NHiTS for individual forecasts and hierarchical modeling

Who this book is forThis book is for data analysts, scientists, and students who want to know how to apply deep learning methods to time-series forecasting problems with PyTorch for real-world business problems.
While the book assumes some understanding of statistics and modeling, you won't need in-depth knowledge of time-series to follow along. Some awareness of Python programming is important, but we do not assume any prior knowledge of PyTorch.
The main goal of this book is to be accessible for those with little or no experience with deep learning methods in time series.

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