Time Series Analysis with Python Cookbook: Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation , Second Edition

Tarek A. Atwan

€18.99 per month

eBook Feb 2026 98 pages 2nd Edition

Tarek A. Atwan

€18.99 per month

eBook Feb 2026 98 pages 2nd Edition

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Time Series Analysis with Python Cookbook

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When embarking on a journey to learn coding in Python, you will often find yourself following instructions to install packages and import libraries, followed by a flow of a code-along stream. Yet an often-neglected part of any data analysis or data science process is ensuring that the right development environment is in place. Therefore, it is critical to have the proper foundation from the beginning to avoid any future hassles, such as an overcluttered implementation or package conflicts and dependency crisis. Having the right environment setup will serve you in the long run when you complete your project, ensuring you are ready to package your deliverable in a reproducible and production-ready manner.

Such a topic may not be as fun and may feel administratively heavy as opposed to diving into the core topic or the project at hand. But it is this foundation that differentiates a seasoned developer from the pack. Like any...

Installing Python libraries

In the preceding recipe, you were introduced to the YAML environment configuration file, which allows you to create a Python virtual environment and all the necessary packages in one step using one line of code:

$ conda env create -f env.yml

Throughout this book, you will need to install several Python libraries to follow the recipes. There are several methods for installing Python libraries, which you will explore in this recipe.

Getting ready

You will create and use different files in this recipe, including a requirements.txt, environment_history.yml, and other files. These files are available to download from the GitHub repository for this book: https://github.com/PacktPublishing/Time-Series-Analysis-with-Python-Cookbook/tree/main/code/Ch1

In this chapter, you will become familiar with how to generate your requirements.txt file, as well as installing libraries in general.

How to do it…

The easiest way to install a collection of libraries at once...

Key benefits

Explore up-to-date forecasting and anomaly detection techniques using statistical, machine learning, and deep learning algorithms
Learn different techniques for evaluating, diagnosing, and optimizing your models
Work with a variety of complex data with trends, multiple seasonal patterns, and irregularities

Description

To use time series data to your advantage, you need to be well-versed in data preparation, analysis, and forecasting. This fully updated second edition includes chapters on probabilistic models and signal processing techniques, as well as new content on transformers. Additionally, you will leverage popular libraries and their latest releases covering Pandas, Polars, Sktime, stats models, stats forecast, Darts, and Prophet for time series with new and relevant examples. You'll start by ingesting time series data from various sources and formats, and learn strategies for handling missing data, dealing with time zones and custom business days, and detecting anomalies using intuitive statistical methods. Further, you'll explore forecasting using classical statistical models (Holt-Winters, SARIMA, and VAR). Learn practical techniques for handling non-stationary data, using power transforms, ACF and PACF plots, and decomposing time series data with multiple seasonal patterns. Then we will move into more advanced topics such as building ML and DL models using TensorFlow and PyTorch, and explore probabilistic modeling techniques. In this part, you’ll also learn how to evaluate, compare, and optimize models, making sure that you finish this book well-versed in wrangling data with Python.

Who is this book for?

This book is for data analysts, business analysts, data scientists, data engineers, and Python developers who want practical Python recipes for time series analysis and forecasting techniques. Fundamental knowledge of Python programming is a prerequisite. Prior experience working with time series data to solve business problems will also help you to better utilize and apply the different recipes in this book.