May 2026
Intermediate to advanced
606 pages
16h 23m
English
Overview
Discover how to harness the power of PyTorch for time-series forecasting challenges with 'Time Series with PyTorch.' Learn to apply deep learning techniques to time-series data, from understanding neural network fundamentals to tackling complex, real-world projects. By combining foundational knowledge with practical, hands-on examples, this book is your guide to becoming proficient in applying PyTorch to time-series problems.
What this Book will help me do
- Master coding and testing neural networks using PyTorch and PyTorch Lightning effectively.
- Learn to adapt neural architectures to address challenges from different time-series data structures.
- Understand model evaluation methods, from comparison and optimization to proper data partitioning.
- Gain insights into the workings of state-of-the-art time-series models and when to use them effectively.
- Apply advanced techniques like TFT, NBEATs, and others for cutting-edge time-series modeling tasks.
Author(s)
Graeme Davidson and Lei Ma are experienced practitioners with extensive backgrounds in machine learning and time-series analysis. Graeme has authored numerous publications on applying neural networks to real-world problems, while Lei specializes in leveraging both traditional and contemporary statistical methods in conjunction with deep learning. Together, they offer a blend of cutting-edge expertise, practical insights, and a clear pedagogical style to guide readers through the intricate world of time-series deep learning.
Who is it for?
This book is intended for data analysts, scientists, and students interested in using deep learning for time-series forecasting within real-world contexts. Readers should have a basic understanding of Python programming and statistics to benefit fully from this book. Those new to PyTorch will find this an accessible starting point for exploring its applications to time-series problems. If you're looking to advance your skills in applying deep learning to complex data types, this resource is designed for you.
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