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Time Series Analysis with Python Cookbook

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

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Product type Paperback
Published in Jun 2022
Publisher Packt
ISBN-13 9781801075541
Length 630 pages
Edition 1st Edition
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Author (1):
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Tarek A. Atwan Tarek A. Atwan
Author Profile Icon Tarek A. Atwan
Tarek A. Atwan
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Table of Contents (18) Chapters Close

Preface 1. Chapter 1: Getting Started with Time Series Analysis 2. Chapter 2: Reading Time Series Data from Files FREE CHAPTER 3. Chapter 3: Reading Time Series Data from Databases 4. Chapter 4: Persisting Time Series Data to Files 5. Chapter 5: Persisting Time Series Data to Databases 6. Chapter 6: Working with Date and Time in Python 7. Chapter 7: Handling Missing Data 8. Chapter 8: Outlier Detection Using Statistical Methods 9. Chapter 9: Exploratory Data Analysis and Diagnosis 10. Chapter 10: Building Univariate Time Series Models Using Statistical Methods 11. Chapter 11: Additional Statistical Modeling Techniques for Time Series 12. Chapter 12: Forecasting Using Supervised Machine Learning 13. Chapter 13: Deep Learning for Time Series Forecasting 14. Chapter 14: Outlier Detection Using Unsupervised Machine Learning 15. Chapter 15: Advanced Techniques for Complex Time Series 16. Index 17. Other Books You May Enjoy

Converting DateTime with time zone information

When working with time-series data that requires attention to different time zones, things can get out of hand and become more complicated. For example, when developing data pipelines, building a data warehouse, or integrating data between systems, dealing with time zones requires attention and consensus amongst the different stakeholders in the project. For example, in Python, there are several libraries and modules dedicated to working with time zone conversion; these include pytz, dateutil, and zoneinfo, to name a few.

Let's discuss an inspiring example regarding time zones within time-series data. It is common for large companies that span their products and services across continents to include data from different places around the globe. For example, it would be hard to make data-driven business decisions if we neglect time zones. Let's say you want to determine whether most customers come to your e-commerce site in...

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