Time Series Analysis with Python Cookbook: Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation

by Tarek A. Atwan

Overview

Perform time series analysis and forecasting confidently with this Python code bank and reference manual Purchase of the print or Kindle book includes a free PDF eBook

Key Features

• Explore up-to-date forecasting and anomaly detection techniques using statistical, machine learning, and deep learning algorithms
• Learn different techniques for evaluating, diagnosing, and optimizing your models
• Work with a variety of complex data with trends, multiple seasonal patterns, and irregularities

Book Description

To use time series data to your advantage, you need to be well-versed in data preparation, analysis, and forecasting. This fully updated second edition includes chapters on probabilistic models and signal processing techniques, as well as new content on transformers. Additionally, you will leverage popular libraries and their latest releases covering Pandas, Polars, Sktime, stats models, stats forecast, Darts, and Prophet for time series with new and relevant examples. You'll start by ingesting time series data from various sources and formats, and learn strategies for handling missing data, dealing with time zones and custom business days, and detecting anomalies using intuitive statistical methods. Further, you'll explore forecasting using classical statistical models (Holt-Winters, SARIMA, and VAR). Learn practical techniques for handling non-stationary data, using power transforms, ACF and PACF plots, and decomposing time series data with multiple seasonal patterns. Then we will move into more advanced topics such as building ML and DL models using TensorFlow and PyTorch, and explore probabilistic modeling techniques. In this part, you’ll also learn how to evaluate, compare, and optimize models, making sure that you finish this book well-versed in wrangling data with Python.

What you will learn

• Understand what makes time series data different from other data
• Apply imputation and interpolation strategies to handle missing data
• Implement an array of models for univariate and multivariate time series
• Plot interactive time series visualizations using hvPlot
• Explore state-space models and the unobserved components model (UCM)
• Detect anomalies using statistical and machine learning methods
• Forecast complex time series with multiple seasonal patterns
• Use conformal prediction for constructing prediction intervals for time series

Who this book is for

This book is for data analysts, business analysts, data scientists, data engineers, and Python developers who want practical Python recipes for time series analysis and forecasting techniques. Fundamental knowledge of Python programming is a prerequisite. Prior experience working with time series data to solve business problems will also help you to better utilize and apply the different recipes in this book.

Product Details

ISBN-13:	9781805124283
Publisher:	Packt Publishing
Publication date:	01/23/2026
Product dimensions:	75.00(w) x 92.50(h) x (d)

About the Author

Tarek A. Atwan is a data analytics expert with over 16 years of international consulting experience, providing subject matter expertise in data science, machine learning operations, data engineering, and business intelligence. He has taught multiple hands-on coding boot camps, courses, and workshops on various topics, including data science, data visualization, Python programming, time series forecasting, and blockchain at various universities in the United States. He is regarded as a data science mentor and advisor, working with executive leaders in numerous industries to solve complex problems using a data-driven approach.

Table of Contents

Table of Contents

1. Getting Started with Time Series Analysis
2. Reading Time Series Data from Files
3. Reading Time Series Data from Databases
4. Persisting Time Series Data to Files
5. Persisting Time Series Data to Databases
6. Working with Date and Time in Python
7. Handling Missing Data
8. Outlier Detection Using Statistical Methods
9. Exploratory Data Analysis & Diagnosis
10. Building Univariate Models using Statistical Methods
11. Advanced Statistical Modeling Techniques for Time Series
12. Forecasting Using Supervised Machine Learning
13. Deep Learning for Time Series Forecasting
14. Outlier Detection Using Unsupervised Machine Learning
15. Working with Multiple Seasonality in Time Series
16. Probabilistic Models for Time Series
17. Signal Processing Techniques for Time Series Analysis