Modern Time Series Forecasting Techniques

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

1. Preface
2. Introduction
3. Prophet for business forecasting
4. Tutorial I
5. Tutorial II
6. Change Point Detection in Time Series
7. Monte Carlo Simulation for Probabilistic Forecasting
8. Quantile Regression for Probabilistic Forecasting
9. Conformal Predictions for Probabilistic Forecasting
10. Conformalized Quantile Regression for Probabilistic Forecasting
11. Automatic ARIMA!
12. Time Series Data Formats Made Easy
13. Linear Regression for Multi-period Probabilistic Forecasting
14. Feature Engineering for Tree-based Time Series Models
15. Two Primary Strategies for Multi-period Time Series Forecasting
16. Tree-based XGB, LightGBM, and CatBoost Models for Multi-period Probabilistic Forecasting
17. The Progression of Time Series Modeling Techniques
18. Deep Learning-based DeepAR for Probabilistic Forecasting
19. Application — Probabilistic Predictions for stock prices
20. From RNN to Transformer-based Time Series Models
21. Temporal Fusion Transformer for Interpretable Time Series Predictions
22. 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.
• 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.