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Forecasting Professional
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The following 2 books are included in this bundle...
These books have a total suggested price of $140. Get them now for only $149.95!
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Mastering Modern Time Series Forecasting
A Comprehensive Guide to Statistical, Machine Learning, and Deep Learning Models in Python
11 chapters. 762 pages. 264 references. 119 figures. From foundations to foundation models — in one book.
This is the forecasting reference that didn’t exist until now.
Most forecasting books fall into one of two traps: either they rehash ARIMA and exponential smoothing as if deep learning never happened, or they throw transformer architectures at you with no evaluation discipline and no explanation of when classical methods still win. This book does neither.
Mastering Modern Time Series Forecasting is an end-to-end, practitioner-first guide to building forecasting systems that hold up in production. It covers the full stack — classical statistical models, feature-engineered ML, deep learning, transformers, and modern foundation time series models — with the evaluation and validation rigour that most books skip entirely.
Written by Valery Manokhin, PhD, MBA, CQF. PhD in Machine Learning from Royal Holloway, University of London, supervised by Professor Vladimir Vovk (the creator of Conformal Prediction). MSc in Computational Statistics and Machine Learning from UCL. Years of building and auditing production forecasting systems — and seeing exactly how projects fail when evaluation is wrong, assumptions are ignored, or shiny models replace engineering discipline.
Probabilistic Forecasting with Conformal Prediction in Python
The Practical Guide to Uncertainty Quantification for Data Science, Machine Learning, and Forecasting
Probabilistic Forecasting with Conformal Prediction in Python (Early Access)
The Practical Guide to Uncertainty Quantification for Data Science, Machine Learning, and Forecasting
Confident forecasts aren’t just about accuracy — they’re about knowing when you might be wrong.
This book takes you deep into the fast-growing world of probabilistic forecasting and conformal prediction — modern tools that let you move beyond point estimates to deliver prediction intervals, risk measures, and trustworthy AI decisions.
Whether you’re a data scientist, ML engineer, finance professional, or academic researcher, you’ll learn how to:
- Understand the theory behind conformal prediction and probabilistic forecasting — without unnecessary math overload.
- Apply these methods in real-world projects: from demand forecasting to portfolio risk modeling.
- Implement solutions in Python, step-by-step.
- Build forecasting models that communicate uncertainty clearly to decision-makers.
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