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

Evaluating vector autoregressive (VAR) models

After fitting a VAR model, the next step is to evaluate how well the model captures the interactions and dynamic relationships between the different endogenous variables (multiple time series). Understanding these relationships can help you asses causality, how one variable influences another, and how shocks to one variable propagate through the system.

In this recipe, you will continue where you left off from the previous recipe, Forecasting multivariate time series data using VAR, and explore various diagnostic tools to deepen your understanding of the VAR model. Specifically, test for granger causality, analyze Residual Autocorrelation Function (ACF) plots, use the Impulse Response Function (IRF), and perform Forecast Error Variance Decomposition (FEVD).

These evaluation steps will help you understand both the causal relationships and the interdependencies between the variables in your system, ensuring that your model captures the underlying...