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

Handling missing data with multivariate imputation

Earlier, we discussed the fact that there are two approaches to imputing missing data: univariate imputation and multivariate imputation.

As you have seen in the previous recipes, univariate imputation involves using one variable (column) to substitute for the missing data, disregarding other variables in the dataset. Univariate imputation techniques are usually faster and simpler to implement, but a multivariate approach may produce better results in most situations.

Instead of using a single variable (column), in a multivariate imputation, the method uses multiple variables within the dataset to impute missing values. The idea is simple: Have more variables within the dataset chime in to improve the predictability of missing values.

In other words, univariate imputation methods handle missing values for a particular variable in isolation of the entire dataset and just focus on that variable to derive the estimates. In multivariate imputation...