Advanced Data Modeling and Forecasting with Python
Time Series, Advanced Modeling, and Real-World Systems
This book extends data modeling and machine learning into more complex and realistic settings.
Building on foundational workflows, this book introduces advanced techniques for handling temporal data, uncovering hidden structure, addressing real-world data challenges, and deploying models into production environments.
Rather than focusing on isolated methods, it presents a unified perspective on designing, evaluating, and maintaining complete data science systems.
📊 What you will learn:
- Analyze and forecast time series data using statistical and machine learning approaches
- Discover structure in data through clustering and representation learning
- Handle imbalanced and noisy datasets in practical scenarios
- Combine models using advanced and hybrid modeling techniques
- Deploy models using APIs and production pipelines
- Monitor performance, detect drift, and maintain models over time
- Apply advanced modeling techniques to business and financial problems
This book is designed for readers ready to move from building models to developing reliable, scalable, and production-ready data science solutions.
Citation:
Wei, Shouke. 2026. Advanced Data Modeling and Forecasting with Python: Time Series, Advanced Modeling, and Real-World Systems. 1st ed. Abbotsford, BC: Deepsim Press. https://doi.org/10.5281/zenodo.20043592.
@book{Wei2026datamodel,
author = {Wei, Shouke},
title = {Advanced Data Modeling and Forecasting with {Python}: Time Series, Advanced Modeling, and Real-World Systems},
edition = {1st},
publisher = {Deepsim Press},
address = {Abbotsford, BC},
year = {2026},
doi = {10.5281/zenodo.20043592},
url = {https://press.deepsim.ca},
isbn = {978-1-0675592-6-7},
note = {Also available in hardcover (978-1-0675592-7-4) and paperback (978-1-0675592-8-1) editions.}
}
Publication Details
- Author: Shouke Wei
- Publisher: Deepsim Press
- Series: Practical Data Science with Python
- Format: PDF (Digital)
- Edition: First edition
- Print length: 543 pages
- Item Weight: PDF14.7 MB (15,463,975 bytes)
- Dimensions: 7.24 x 1.35 x 10.24 inches
- Language: English
- ISBN: 978-1-0675592-6-7 (eBook) | 978-1-0675592-7-4 (Hardcover) | 978-1-0675592-8-1 (Paperback)
- DOI: 10.5281/zenodo.20043592
- Publication date: 06/05/2026
- Book 3 of 3: Practical Data Science with Python

