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

Join our book community on Discord

https://packt.link/zmkOY

It is common that, after completing a data analysis task, in which data is extracted from a source system, processed, transformed, and possibly modeled, the output is stored in a database for persistence. You can always store the data in a flat file or export it to a CSV, but when dealing with a large amount of corporate data (including proprietary data), you will need a more robust and secure way to store it. Databases offer several advantages: security (encryption at rest), concurrency (allowing many users to query the database without impacting performance), fault tolerance, ACID compliance, optimized read-write mechanisms, distributed computing, and distributed storage.

In a corporate context, once data is stored in a database, it can be shared across different departments; for example, finance, marketing, sales, and product development can now access the data stored for their own needs. Furthermore, the data can now be...