About the book
"The book is a testament to Kuo’s deep understanding of time series analysis and its applications in predictive analytics and anomaly detection. This book equips readers with the necessary skills to tackle real-world challenges. It is particularly valuable for those seeking a career change into data science. Kuo provides a detailed exploration of both traditional and cutting-edge techniques. Kuo integrates discussions on neural networks and other advanced algorithms, reflecting the latest trends and developments in the field. This ensures that readers are not only learning established methods but are also prepared to engage with the most current and innovative techniques in data science.The book’s clarity and accessibility are enhanced by Kuo’s engaging writing style. He successfully demystifies complex mathematical and statistical concepts, making them approachable without sacrificing rigor."
TABLE OF CONTENTS
- Preface
- Introduction
- Prophet for business forecasting
- Tutorial I
- Tutorial II
- Change Point Detection in Time Series
- Monte Carlo Simulation for Probabilistic Forecasting
- Quantile Regression for Probabilistic Forecasting
- Conformal Predictions for Probabilistic Forecasting
- Conformalized Quantile Regression for Probabilistic Forecasting
- Automatic ARIMA!
- Time Series Data Formats Made Easy
- Linear Regression for Multi-period Probabilistic Forecasting
- Feature Engineering for Tree-based Time Series Models
- Two Primary Strategies for Multi-period Time Series Forecasting
- Tree-based XGB, LightGBM, and CatBoost Models for Multi-period Probabilistic Forecasting
- The Progression of Time Series Modeling Techniques
- Deep Learning-based DeepAR for Probabilistic Forecasting
- Application — Probabilistic Predictions for stock prices
- From RNN to Transformer-based Time Series Models
- Temporal Fusion Transformer for Interpretable Time Series Predictions
- Lag-Llama for Time Series Forecasting
WHAT'S NEW IN THE 2ND EDITION
- Comprehensive and in-depth coverage: The second edition offers a wide range of techniques for anomaly detection. It ensures a well-rounded understanding of the foundational concepts, making it suitable for beginners and seasoned professionals alike. Each chapter builds on the previous one, creating a cohesive and comprehensive learning experience.
- Enhanced explanations and visual presentations: We’ve enriched this edition with a wealth of visualizations. These enhancements are designed to clarify complex concepts. You’ll learn not just the ‘how’ but also the ‘why’ behind each method.
- Supervised and unsupervised learning techniques: We expand the second edition to cover the advanced machine learning techniques for anomaly detection. On the unsupervised learning part, you will be equipped with a wide range of modern techniques to discover new patterns. On the supervised learning part, you will gain a deeper understanding into grid-search, hyperparameter tuning, regularization, under-sampling and over-sampling techniques. You will use the code examples to build a suite of high-power machine learning models.
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200+ data science Q&A: One of the standout features of the second edition is the inclusion of the Q&A sections in each chapter. By working through these questions, you’ll be well-prepared to showcase your expertise and secure your next job opportunity.

Learn with Professor Chris Kuo
Chris Kuo is a seasoned data science professional and adjunct professor with over 25 years of experience applying advanced analytics across multiple industries. He has led high-impact data science initiatives in customer analytics, healthcare, fraud detection, and litigation support, and is the inventor of a U.S. patent in data-driven solutions. Throughout his career, he has held leadership roles at several Fortune 500 companies in the insurance and retail sectors.
In addition to his industry work, Chris Kuo also teaches at Columbia University and has previously taught at Boston University, the University of New Hampshire, and Liberty University, covering subjects such as time series forecasting, mathematical finance, economics, and management. He holds a Ph.D. in Economics from the State University of New York at Stony Brook and a B.S. in Nuclear Engineering from National Tsing Hua University in Taiwan. He currently resides in New York City with his wife.