Time Series Analysis with Python Cookbook, 2nd Edition
Edition: Time Series Analysis with Python Cookbook is a practical reference for working through time-series data preparation, analysis, forecasting, and anomaly detection in Python. Its recipes span classical statistics, machine learning, deep learning, probabilistic forecasting, and model evaluation, with fundamental Python knowledge expected.
At a glance
- Recipes cover data ingestion, missing values, time zones, business calendars, outlier detection, and exploratory analysis.
- Topics include Holt-Winters, SARIMA, VAR, state-space models, and forecasting series with multiple seasonal patterns.
- The second edition adds material on probabilistic methods, signal processing, and transformers, alongside machine-learning and deep-learning approaches.
- The listing says print or Kindle purchases include a free PDF eBook.
Reference price: $59.99
Price checked: Nov 14, 2026
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Pros and cons
Pros
- Broad workflow coverage goes from data preparation and storage through forecasting, evaluation, and deployment-related topics.
- Examples draw on multiple Python libraries and modeling approaches, giving readers material for comparing methods rather than following only one model family.
- Includes practical treatment of missing data, anomaly detection, uncertainty estimates, and interactive time-series visualization.
Cons
- The book expects fundamental Python programming knowledge, so it is not positioned as a first programming text.
Technical specifications
9 specifications| Edition | 2nd ed. |
|---|---|
| Publisher | Packt Publishing |
| Publication date | January 16, 2026 |
| Language | English |
| Print length | 812 pages |
| ISBN-10 | 1805124285 |
| ISBN-13 | 978-1805124283 |
| Weight listed | 3.02 pounds |
|---|---|
| Dimensions | 7.5 x 1.83 x 9.25 inches |
What to know before buying
- Prior experience applying time-series data to business problems is described as helpful, though not required. The listing also mentions exclusive GitHub bonus chapters and recipes, so access details for those extras may depend on the edition or purchase format.
Who is it for?
A good fit for
- Python developers and data analysts seeking practical time-series recipes.
- Data scientists and data engineers who need workflows spanning ingestion, modeling, and evaluation.
- Readers familiar with Python who want to compare statistical, machine-learning, deep-learning, and probabilistic forecasting approaches.
Less suited to
- People seeking an introductory Python programming book.
- Readers who want a purely theoretical treatment rather than hands-on recipes.
Customer reviews on Amazon
- 5 stars
78% 78% - 4 stars
12% 12% - 3 stars
0% 0% - 2 stars
0% 0% - 1 star
10% 10%
Aggregate customer ratings lean positive, with many five-star ratings, but a smaller one-star share indicates that experiences are not uniformly favorable. With a limited rating sample and no written feedback provided, these figures cannot identify what readers liked or disliked. Consider the book's substantial length and its expectation of basic Python knowledge when judging fit.
Frequently asked questions
What experience does this book expect?
The listing says readers need fundamental Python programming knowledge. Prior experience using time-series data to solve business problems can help them apply the recipes more quickly.
What topics does the book cover?
It covers data ingestion and preparation, missing values, time handling, anomaly detection, statistical and machine-learning forecasting, deep learning, probabilistic methods, model evaluation, and visualization.
Does it include a PDF eBook?
The listing claims that purchasing the print or Kindle book includes a free PDF eBook. Confirm the terms for the specific format and listing before purchase.
Is this a beginner's programming book?
No. The described audience is expected to have fundamental Python programming knowledge.
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