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

Applying power transformations

Time series data can be complex, and embedded within the data is critical information that you will need to understand and peek into to determine the best approach for building a model. For example, you have explored time series decomposition, understood the impact of trend and seasonality, and tested for stationarity. In the previous recipe, Detecting time series stationarity, you examined the technique to transform data from non-stationary to stationary. This includes the idea of detrending, which attempts to stabilize the mean over time.

Depending on the model and analysis you are pursuing, you may need to test for additional assumptions against the observed dataset or the model's residuals. For example, testing for homoskedasticity (also spelled homoscedasticity) and normality. Homoskedasticity means that the variance is stable over time. More specifically, it is the variance of the residuals. When the variance is not constant, changing over time...