You're reading from Time Series Analysis with Python Cookbook Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation

Product type Paperback

Published in Apr 2025

Publisher

ISBN-13 9781805124283

Length 98 pages

Edition 2nd Edition

Languages

Python

Concepts

Data Analysis

Author (1):

Tarek A. Atwan

View More author details

Table of Contents (13) Chapters

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

2. Getting Started with Time Series Analysis

3. Reading Time Series Data from Files

4. Reading Time Series Data from Databases

5. Persisting Time Series Data to Files

6. Persisting Time Series Data to Databases

7. Working with Date and Time in Python

8. Handling Missing Data

9. Outlier Detection Using Statistical Methods

10. Exploratory Data Analysis and Diagnosis

11. Building Univariate Time Series Models Using Statistical Methods

12. Additional Statistical Modeling Techniques for Time Series

13. Outlier Detection Using Unsupervised Machine Learning

Technical requirements

In this chapter, we will extensively use pandas 2.2.2 (released April 10, 2024).

Throughout our journey, you will install several Python libraries to work with pandas. These are highlighted in the Getting ready section for each recipe. You can also download the Jupyter notebooks from the GitHub repository at https://github.com/PacktPublishing/Time-Series-Analysis-with-Python-Cookbook to follow along.

You should refer to the Technical Requirements section in Chapter 3, Reading Time Series Data from Databases. This includes creating a configuration file such as the database.cfg.

You will be using the same dataset throughout the recipes in this chapter. The dataset is based on Amazon's stock data from January 2019 to December 2023 pulled using the yfnance library and written as a pandas DataFrame.

Start by installing the yfinance library, which you can install using conda with: