Modern Time Series Anomaly Detection: With Python & R Code Examples Paperback – November 12, 2022

This book walks me through a wealth of time series techniques, ranging from the essential ones such as SMA, Exponential Smoothing, STD, Prophet Module, Kalman Filter, GAM, and ARIMA, to more advanced tools related to the neural network, such as RNN, LSTM, and GRU.Professor Kuo explains these complex concepts and algorithms with detailed descriptions, understandable examples, and interesting stories and analogies. In addition, the sample datasets, codes, and projects are great resources for me to practice and become familiar with these tools.Above all, this book covers broad topics in time series modeling, forecasting, and anomaly detection and is suitable for beginners to get hands-on practice. I highly recommend it!

The book covers various time series models, while the authors include hands-on projects in the latter part of the book as the concepts and exercises become more professional. I enjoyed reading those concepts and principles behind the algorithm while getting exposure to the real code in Python and R for hands-on experience! Really recommend!

If you want to learn time-series modeling thoroughly, this is the book to read. Prof Chris Kuo clearly explained time-series algorithms ranging from ARIMA and the Prophet model to more advanced deep learning models. The book is well-structured and easy to read, with R and Python code to guide you along. It was applicable to my work. Recommend for both new and experienced data scientists.

I studied Anomaly Detection under Prof Chris Kuo. He is one of the experts in this area. His books are just an extension of the classes. Reading the book, I felt like I was attending the classes. It is very well written and the concepts are explained very well. He also provides sample R and Python codes to follow along. These codes have come in handy even during my work experience.

This book is used in teaching the anomaly detection course at Columbia University. It explains methods and theories and shows very specific steps. I have successfully applied the methods described in the book to practical applications.

This book is helpful for people who want to learn anomaly detection. It presents concise codes and detailed explanations for code. Take advantage of this book if you're going to learn anomaly detection.

This book includes many possible problem you could meet when you are learning about time series model. With code and instruction, you will definitely become well-prepared for future time series learning!

This book is really helpful if you are interested in Modern Time Series Analysis and (Supervised/unsupervised) Machine Learning as well as Deep Learning (in various industry contexts). For time series analysis, I learned how to use sophisticated time series models like the Facebook prophet model. For ML, this book covers almost every stage of a data science project. One thing I really want to mention is that this book really taught me how to do feature engineering (a very important step in ML), what model to select, and how to make the model performance better (with advanced tools like H2O.ai). Anyway, this book will make you feel much more confident doing real-world data analysis and data science tasks.