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

Working with date offsets

When working with time series, it is critical that you learn more about the data you are working with and how it relates to the problem you are attempting to solve. For example, when working with manufacturing or sales data, you cannot assume that an organization's working day is Monday to Friday or whether it uses the standard calendar year or fiscal year. You should also consider understanding any holiday schedule, annual shutdowns, and other matters related to the business operation.

This is where offsets can be handy. They can help transform your dates into something more meaningful and relatable to a business. They can also help correct data entries that may not be logical.

We will work through a hypothetical example in this recipe and see how to leverage pandas offsets.

How to do it…

In this recipe, you will generate a time-series DataFrame to represent some daily logs of production quantity. The company, a US-based firm, would like to analyze...