Time Series Forecasting Book Released
Valeriy M. published a video walkthrough of Chapter 1 from his time series forecasting book, covering key forecasting concepts in depth with Pro and Core editions available. The educational content has garnered 478 views and targets practitioners looking to improve their market prediction capabilities.
- The author, Dr. Valeriy Manokhin, holds a PhD in Machine Learning, an MBA, and a Certificate in Quantitative Finance (CQF), with experience building forecasting systems for Fortune 500 companies. - The full title of the book is "Mastering Modern Time Series Forecasting: The Complete Guide to Statistical, Machine Learning & Deep Learning Models in Python". - The book's curriculum extends beyond basic concepts to include deep learning, transformers, and modern foundation time series models (FTSMs). - Manokhin is a proponent of Conformal Prediction, a framework for quantifying uncertainty in machine learning, and is the creator of the "Awesome Conformal Prediction" repository on GitHub. - He has authored seven books focusing on forecasting and conformal prediction, aiming to bridge the gap between academic research and real-world implementation. - A core theme of the author's work is avoiding "silent failures" in forecasting by focusing on robust evaluation metrics and building systems that deliver measurable business impact. - In addition to his books, Dr. Manokhin teaches advanced courses on forecasting and machine learning on the Maven platform. - The book is used by data science leaders in over 100 countries and at companies including Amazon, Google, and Meta.
