Time Series Analysis with Python Cookbook: Practical recipes for the complete time series workflow, from modern data engineering to advanced forecasting and anomaly detection 2nd ed. Edition-电子书百科大全

Time Series Analysis with Python Cookbook: Practical recipes for the complete time series workflow, from modern data engineering to advanced forecasting and anomaly detection 2nd ed. Edition book cover

Time Series Analysis with Python Cookbook: Practical recipes for the complete time series workflow, from modern data engineering to advanced forecasting and anomaly detection 2nd ed. Edition

Author(s): Tarek A. Atwan (Author)

Publisher: Packt Publishing
Publication Date: January 16, 2026
Edition: 2nd ed.
Language: English
Print length: 812 pages
ISBN-10: 1805124285
ISBN-13: 9781805124283

Book Description

Perform time series analysis and forecasting confidently with this Python code bank and reference manual.

Access exclusive GitHub bonus chapters and hands-on recipes covering Python setup, probabilistic deep learning forecasts, frequency-domain analysis, large-scale data handling, databases, InfluxDB, and advanced visualizations.

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 master data preparation, analysis, and forecasting. This fully refreshed second edition helps you unlock insights from time series data with new chapters on probabilistic models, signal processing techniques, and new content on transformers. You’ll work with the latest releases of popular libraries like Pandas, Polars, Sktime, stats models, stats forecast, Darts, and Prophet through up-to-date examples.

You'll hit the ground running 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.

Through detailed instructions, you'll explore forecasting using classical statistical models such as Holt-Winters, SARIMA, and VAR, and learn practical techniques for handling non-stationary data using power transforms, ACF and PACF plots, and decomposing time series data with seasonal patterns. The recipes then level up to cover more advanced topics such as building ML and DL models using TensorFlow and PyTorch and applying probabilistic modeling techniques. In this part, you’ll also be able to evaluate, compare, and optimize models, finishing with a strong command of 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 to learn time series analysis and forecasting techniques step by step through practical Python recipes.

To get the most out of this book, you’ll need fundamental Python programming knowledge. Prior experience working with time series data to solve business problems will help you to better utilize and apply the recipes more quickly.

Getting Started with Time Series Analysis
Reading Time Series Data from Files
Reading Time Series Data from Databases
Persisting Time Series Data to Files
Persisting Time Series Data to Databases
Working with Date and Time in Python
Handling Missing Data
Outlier Detection Using Statistical Methods
Exploratory Data Analysis and Diagnosis
Building Univariate Models Using Statistical Methods

(N.B. Please use the Read Sample option to see further chapters)

Editorial Reviews

Review

“There were many parts of the book that I truly enjoyed: serializing custom functions and plots, creating a custom business calendar for Jordan, using the Hodrick-Prescott filter for separating cyclical components from long-term trends, and explaining the difference between KNN (distance-based) and LOF (density-based) detection using a coffee shop analogy, among others. I really liked this book and recommend it to anyone looking to gain practical experience with time series.”

Andrey Lukyanenko, Kaggle Competition Master and Machine Learning Engineer at Meta

“Time series are prevalent in businesses; therefore, forecasting is a fundamental data science task. The field has evolved in recent years by integrating machine learning models into the established toolkit of statistical approaches. Time Series Analysis with Python Cookbook provides practical tutorials that help accomplish numerous tasks, making it an excellent resource for data professionals.”

Giannis Tolios, Data Scientist, Researcher in Machine Learning, Statistics, and Data Visualization, and Book Author (Leanpub)

“This isn’t a theory-heavy book; it’s a working cookbook. If you already know the ‘why,’ it’s a high-utility companion for the ‘how,’ particularly on the often-overlooked plumbing of time series and quick, runnable implementations across the Python forecasting ecosystem.”

Jeffrey Tackes, Global Head of Forecasting, Principal Data Scientist, Founder of Forecast Academy, and Author of Modern Time Series Forecasting with Python

“Unlike typical time series books that often jump directly to models, this one starts with the practical realities of real projects: swiftly acquiring data, ensuring its quality, and building a trustworthy diagnostic view. It then progresses from traditional forecasting methods to modern machine learning and deep learning approaches, providing actionable recipes for immediate use. This book is an outstanding pragmatic playbook for anyone seeking practical guidance over theory-heavy discussions.”

Luca Zavarella, Microsoft MVP for Data and AI Platform

“Atwan’s second edition unapologetically drills into the mathematical bedrock of forecasting, moving effortlessly from stationarity constraints to probabilistic deep learning methods. This is a non-trivial, distinctively strong toolkit that equips serious practitioners to model complex seasonality's with precision.”

Mike Erlihson, PhD, Head of AI at Stealth Startup, AI Advisory Board Member, and Data Science Podcaster

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.

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