"Modern Time Series Forecasting Techniques" is an insightful and comprehensive guide that stands out as a valuable resource for anyone involved in predictive analytics and data science. The book is meticulously structured, beginning with fundamental concepts and progressively advancing to more sophisticated topics, making it accessible to both beginners and experienced practitioners.
One of the standout features of this book is its comprehensive coverage of Python libraries relevant to time series forecasting. In addition, the book's deep dive into modern techniques, such as Prophet and NeuralProphet, is particularly valuable. It not only explains the mechanics behind these models but also provides detailed guidance on data preparation, modeling, and hyper-parameter tuning. This practical approach ensures that readers can effectively apply these techniques to their own data sets.
Furthermore, the sections on probabilistic forecasting (Part II) and tree-based time series techniques (Part IV) are comprehensive and well-explained. The discussions on Monte Carlo Simulation, quantile regression, and conformal prediction (Chapters 6-9) are thorough and provide a solid foundation for understanding these advanced methods.
The book also excels in its treatment of autoregressive-based techniques (Part III) and deep learning-based methods (Part V). The progression from classical models to deep learning techniques, including LSTM and RNN, is well-structured and informative.
In summary, "Modern Time Series Forecasting Techniques" is a well-rounded and informative book that serves as an excellent reference for both novice and experienced data scientists. Its practical case studies, comprehensive coverage of modern techniques, and detailed explanations make it an indispensable guide in the field of time series forecasting.
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From the Publisher
Chris Kuo
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 For- tune 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.
Chris Kuo is a writer on Medium.com, also known as ’Dr. Dataman’ in the field of data science. He has published in several economic and management journals and is the author of the following data science books (by year):
- 2021 - Modern Time Series Anomaly Detection: With Python and R examples
- 2022 - The eXplainable A.I.: With Python examples
- 2022 - Transfer Learning for Image Classification: With Python examples
- 2023 - The Handbook of Anomaly Detection: Build and modernize your anomaly detectionmodels with examples
- 2023 - The Handbook of NLP with Gensim: Leverage topic modeling to uncover hiddenpatterns, themes, and valuable insights within textual data
- 2024 - Modern Time Series Forecasting: For Predictive Analytics and Anomaly Detection
Modern Time Series Forecasting Techniques For Predictive Analytics and Anomaly Detection
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.
The handbook of NLP with Gensim
This book demystifies NLP and equips you with hands-on strategies spanning healthcare, e-commerce, finance, and more to enable you to leverage Gensim in real-world scenarios.
You'll begin by exploring motives and techniques for extracting text information like bag-of-words, TF-IDF, and word embeddings. This book guides you on topic modeling using methods such as Latent Semantic Analysis (LSA) for dimensionality reduction and discovering latent semantic relationships in text data, Latent Dirichlet Allocation (LDA) for probabilistic topic modeling, and Ensemble LDA to enhance topic modeling stability and accuracy. This book will inspire you to design innovative projects. By the end of this book, you'll have mastered the techniques essential to create applications with Gensim and integrate NLP into your business processes.
The eXplainable A.I.
How AI systems make decisions is not known to most people. Many of the algorithms, though achieving a high level of precision, are not easily understandable for how a recommendation is made. This is especially the case in a deep learning model. As humans, we must be able to fully understand how decisions are made so we can trust the decisions of AI systems. We need ML models to function as expected, to produce transparent explanations, and to be visible in how they work. Explainable AI (XAI) is important research and has been guiding the development of AI. It enables humans to understand the models so as to manage effectively the benefits that AI systems provide, while maintaining a high level of prediction accuracy. Explainable AI answers the following questions to build the trusts of users for the AI systems:
● Why does the model predict that result?
● What are the reasons for a prediction?
● What is the prediction interval?
● How does the model work?
Transfer Learning for Image Classification: With Python Examples
What you will learn
- Learn how deep learning models treat image data.
- Learn what a convolutional neural network (CNN) is.
- Learn each layer of a CNN by visualizing what it sees in an image layer-by-layer
- Learn the development of pre-trained image models
- Learn how to annotate images programmatically and pre-process images for modeling
- Follow Step 1,2,3 in the book to build deep learning models with Keras
- Build a repeatable pipeline to apply transfer learning for any image projects
This book is for interested readers who want to learn deep learning and image modeling. Readers who do not have deep learning backgrounds will find this book accessible. It is suitable for data scientists in fields such as business, finance, insurance, engineering, science, or biomedical science. Data science instructors may find this book ideal for a semester-long capstone project.
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Great Book for Mastering Time Series Analysis and Forecasting
This book is an invaluable resource for anyone interested in mastering time series analysis and forecasting. It offers a comprehensive and meticulously organized exploration of time series models and applications, making complex concepts accessible. Each part of the book is thoughtfully crafted, guiding readers from foundational techniques like Prophet and NeuralProphet to advanced methodologies including deep learning and transformer-based approaches. A MUST-READ for anyone looking to excel in the field of time series forecasting and anomaly detection.
