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 YOU GET IN THE BOOK
- Learning time series techniques comprehensively in a short period of time
- A roadmap from the classical techniques to modern time series forecasting
- Applying forecasting for resource planning and anomaly detection
- Mastering time series Python libraries
- Model interpretability
- Model evaluation metrics
- Hands-on example code
- Cheat Sheets
Learn with Chris Kuo
Chris Kuo is a data scientist and an adjunct professor with 23+ years of experience. He led various data science solutions including customer analytics, health data science, fraud detection, and litigation analytics. He is also an inventor of a U.S. patent. He has worked at several Fortune 500 companies in the insurance and retail industries. In addition to teaching at Columbia University, he has taught courses in time series forecasting, mathematical finance, economics, and management at Boston University, University of New Hampshire, and Liberty University. Chris Kuo received his Ph.D. in Economics from SUNY at Stony Brook and his B.S. in Nuclear Engineering from National Tsing-Hua University, Taiwan. He and his wife live in New York, New York.