You're reading from Time Series Analysis with Python Cookbook Practical recipes for the complete time series workflow, from modern data engineering to advanced forecasting and anomaly detection

Product type Paperback

Published in Jan 2026

Publisher Packt

ISBN-13 9781805124283

Length 812 pages

Edition 2nd Edition

Languages

Python

Concepts

Data Analysis

Author (1):

Tarek A. Atwan

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Table of Contents (18) Chapters

Preface

1. Reading Time Series Data from Files

2. Reading Time Series Data from Databases FREE CHAPTER

3. Persisting Time Series Data to Files

4. Persisting Time Series Data to Databases

5. Working with Date and Time in Python

6. Handling Missing Data

7. Outlier Detection Using Statistical Methods

8. Exploratory Data Analysis and Diagnosis

9. Building Univariate Time Series Models Using Statistical Methods

10. Additional Statistical Modeling Techniques for Time Series

11. Forecasting Using Supervised Machine Learning

12. Deep Learning for Time Series Forecasting

13. Outlier Detection Using Unsupervised Machine Learning

14. Advanced Techniques for Complex Time Series

15. Unlock Your Exclusive Benefits

Unlock this Book’s Free Benefits in 3 Easy Steps

16. Other Books You May Enjoy

17. Index

Technical requirements

You will work with the sktime library, described on its GitHub repository as “a unified framework for machine learning with time series.” Behind the scenes, sktime is a wrapper around other popular ML and time-series libraries, including scikit-learn.

Creating a new Python environment when installing new libraries such as sktime is always a good idea. If you need a quick refresher on creating a virtual Python environment, check out the Development environment setup recipe in Chapter 0 on GitHub, which covers two methods: using conda and venv.

To install sktime using pip, you can run the following:

pip install "sktime[all_extras]"

This command installs all optional dependencies, which can be resource-intensive. If you encounter issues, consider installing only the necessary components (e.g., sktime[forecasting]).