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

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As a data scientist, data analyst, or business analyst, you have probably discovered that hoping to obtain a perfect clean dataset is too optimistic. What is more common, though, is that the data you are working with suffers from flaws such as missing values, erroneous data, duplicate records, insufficient data, or the presence of outliers in the data.

Time series data is no different, and before plugging the data into any analysis or modeling workflow, you must investigate the data first. It is vital to understand the business context around the time series data to detect and identify these problems successfully. For example, if you work with stock data, the context is very different from COVID data or sensor data.

Having that intuition or domain knowledge will allow you to anticipate what to expect and what is considered acceptable when analyzing the data. Always try to understand the business context around the data. For...