{"product_id":"time-series-with-pytorch-modern-deep-learning-toolkit-for-real-world-forecasting-challenges-9781805128182","title":"Time Series with PyTorch: Modern Deep Learning Toolkit for Real-World Forecasting Challenges","description":"\u003cp\u003e • Author(s): Graeme Davidson | Lei Ma\u003cbr\u003e • Publisher: Packt Publishing\u003cbr\u003e • Publisher Imprint: Packt Publishing\u003cbr\u003e • BISAC: Data Science - Data Analytics\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eTime 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.\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eKey Features: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e- Grasp core concepts through clear explanations that build genuine understanding rather than surface familiarity\u003c\/p\u003e\u003cp\u003e- Work with realistic datasets and develop the judgement to choose the right approach for your problem\u003c\/p\u003e\u003cp\u003e- Progress from neural network fundamentals to advanced techniques across a full range of time series challenges.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eBook Description: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eNeural 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.\u003c\/p\u003e\u003cp\u003eStarting 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.\u003c\/p\u003e\u003cp\u003eUnlike 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.\u003c\/p\u003e\u003cp\u003eThroughout, the focus is pragmatic: theory is reinforced through experimentation and implementation so you can apply these methods confidently to real-world time-series problems.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWhat You Will Learn: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e- Build, train, and evaluate neural networks for time series using PyTorch and PyTorch Lightning. Tune models with Bayesian optimisation and validate them with suitable metrics and strategies.\u003c\/p\u003e\u003cp\u003e- Progress from feedforward and recurrent networks to transformers and models such as N-BEATS, N-HiTS, and TFT.\u003c\/p\u003e\u003cp\u003e- Learn how global models use cross- and transfer learning across many series.\u003c\/p\u003e\u003cp\u003e- Generate synthetic series and representations with diffusion and self-supervised methods.\u003c\/p\u003e\u003cp\u003e- Apply modern approaches to classification, clustering, and anomaly detection.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWho this book is for: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eThis 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.\u003c\/p\u003e\u003cp\u003eWhile the book assumes some understanding of statistics and modeling, you won't need in-depth knowledge of time series to follow along. Some familiarity with Python is important, but we do not assume any prior knowledge of PyTorch.\u003c\/p\u003e\u003cp\u003eThe main goal of this book is to be accessible to those with little or no experience with deep learning methods in time series.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eTable of Contents\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e- Time Series for Everyone\u003c\/p\u003e\u003cp\u003e- The Challenge of Time Series\u003c\/p\u003e\u003cp\u003e- Evaluating Time-Series Models\u003c\/p\u003e\u003cp\u003e- PyTorch Fundamentals\u003c\/p\u003e\u003cp\u003e- Simple Neural Architecture\u003c\/p\u003e\u003cp\u003e- Optimization\u003c\/p\u003e\u003cp\u003e- Conformal Prediction\u003c\/p\u003e\u003cp\u003e- Recurrent Neural Networks\u003c\/p\u003e\u003cp\u003e- Transformers\u003c\/p\u003e\u003cp\u003e- Other Neural Structures\u003c\/p\u003e\u003cp\u003e- Transfer Learning and Global Modeling\u003c\/p\u003e\u003cp\u003e- Synthetic Time Series Data\u003c\/p\u003e\u003cp\u003e- Diffusion Models\u003c\/p\u003e\u003cp\u003e- Time Series Classification\u003c\/p\u003e\u003cp\u003e- Time Series Clustering\u003c\/p\u003e\u003cp\u003e- Embeddings for Time Series\u003c\/p\u003e\u003cp\u003e- Supervised and Unsupervised Anomaly Detection\u003c\/p\u003e\u003cp\u003e- Self-Supervised Learning for Time Series\u003c\/p\u003e","brand":"Packt Publishing","offers":[{"title":"Paperback","offer_id":47968922337431,"sku":"9781805128182","price":5252.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9781805128182.webp?v=1782919849","url":"https:\/\/atlanticbooks.com\/products\/time-series-with-pytorch-modern-deep-learning-toolkit-for-real-world-forecasting-challenges-9781805128182","provider":"Atlantic Books","version":"1.0","type":"link"}