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Time Series with PyTorch

You're reading from Time Series with PyTorch Modern Deep Learning Toolkit for Real-World Forecasting Challenges

Product type Paperback

Published in May 2026

Last Updated in Sep 2025

Publisher Packt

ISBN-13 9781805128182

Length 610 pages

Edition 1st Edition

Languages

Python

Tools

PyTorch

Concepts

Machine Learning

Authors (2):

Graeme Davidson

Lei Ma

View More author details

Table of Contents (9) Chapters

1. Time Series with PyTorch, First Edition: Modern Deep Learning Toolkit for Real-World Forecasting Challenges

2. Time series for everyone FREE CHAPTER

3. The Challenge of Time series

4. Evaluating time-series models

Defining

5. PyTorch Fundamentals

6. Simple neural architectures

7. Optimisation

8. Conformal prediction

9. Recurrent Neural Networks

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We now have the necessary knowledge of time series and data cleaning/resampling techniques - our next step is to learn about PyTorch building blocks for creating neural architectures and preparing time-series data for them. For newcomers to neural networks some terms may seem daunting but understanding them will help as we explore different architectures in each chapter. This chapter will provide a crash introduction PyTorch.

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Authors (2)

Graeme Davidson

Graeme Davidson

Graeme Davidson is a data scientist working at one of the top demand forecasting platforms as rated by Gartner. He has over a decade of experience in analyzing and modeling with time-series data, from researching consumer motivations with Unilever and the University of Liverpool, to predicting consumer demand at Retail Express.

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Lei Ma

Lei Ma

Lei Ma is a physicist-turned data scientist specializing in time series forecasting. He has tackled real-world forecasting challenges across industries like housing, logistics, ecommerce, and manufacturing. He has led and implemented numerous forecasting projects, and is not only experienced in building advanced time series models but also in providing strategic and holistic insights into time series analysis projects.

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