Time Series with PyTorch: Deep Learning for Forecasting
Time Series with PyTorch is a 606-page technical book on applying deep learning methods to time-series problems. It moves from PyTorch foundations and neural networks through forecasting architectures, evaluation, transfer learning, synthetic data, classification, clustering, and anomaly detection. The book assumes familiarity with Python and some statistics and modeling, but says prior PyTorch knowledge is not required.
At a glance
- Covers PyTorch fundamentals, neural network training, and evaluation for time-series models.
- Includes recurrent networks, transformers, N-BEATS, N-HiTS, and the Temporal Fusion Transformer.
- Explores conformal prediction, Bayesian hyperparameter tuning, transfer learning, and global modeling across series.
- Extends beyond forecasting to synthetic data, self-supervised learning, classification, clustering, embeddings, and anomaly detection.
Reference price: $54.99
Price checked: Nov 14, 2026
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Pros and cons
Pros
- Broad coverage makes it relevant beyond point forecasting, including uncertainty estimation and time-series classification tasks.
- The listing describes a progression from neural-network basics to advanced architectures and practical evaluation.
- Designed for readers with Python familiarity who are new to PyTorch.
Cons
- The book assumes some prior understanding of statistics and modeling, so it is not positioned as a first introduction to those subjects.
Technical specifications
8 specifications| Publisher | Packt Publishing |
|---|---|
| Publication date | May 29, 2026 |
| Language | English |
| Print length | 606 pages |
| ISBN-10 | 1805128183 |
| ISBN-13 | 978-1805128182 |
| Weight listed | 2.26 pounds |
|---|---|
| Dimensions | 7.5 x 1.37 x 9.25 inches |
Who is it for?
A good fit for
- Data analysts, data scientists, and students working with time-series problems.
- Python users seeking a deep-learning-focused treatment of forecasting and related time-series tasks.
- Readers who want to study neural architectures, model evaluation, or cross-series transfer learning in one book.
Less suited to
- Readers looking for a statistics-free introduction to forecasting or a guide that avoids coding.
- Readers without Python familiarity, which the listing says is important.
Customer reviews on Amazon
- 5 stars
85% 85% - 4 stars
0% 0% - 3 stars
0% 0% - 2 stars
0% 0% - 1 star
15% 15%
Aggregate ratings lean strongly positive, with most ratings in the highest category, but a smaller low-star share means the overall picture is not uniformly favorable. The rating data does not explain what readers liked or disliked, so it cannot establish which chapters, explanations, or code examples work best. Consider the broad technical scope and the stated Python and statistics prerequisites when judging whether the book fits your needs.
Frequently asked questions
Does the book require prior PyTorch experience?
The listing says no prior PyTorch knowledge is assumed. It does expect some familiarity with Python and some understanding of statistics and modeling.
Does it cover topics beyond forecasting?
Yes. The listed topics include classification, clustering, anomaly detection, embeddings, synthetic time-series data, and self-supervised learning.
Which forecasting approaches are covered?
The listing names feedforward and recurrent networks, transformers, N-BEATS, N-HiTS, and the Temporal Fusion Transformer, along with model tuning and evaluation.
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