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Mastering Forecasting Metrics & Accuracy: For Data Science and Beyond
This book is 10% completeLast updated on 2025-10-01
Mastering Forecasting Metrics & Accuracy: For Data Science and Beyond Forecasting models are only as good as the metrics used to measure them. Yet many teams still rely on outdated or misleading measures like MAPE. This book is the first comprehensive, practitioner-friendly guide dedicated entirely to forecast evaluation metrics — blending clear theory, Python recipes, and real-world case…
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Mastering Forecasting Metrics & Accuracy: For Data Science and Beyond
Forecasting models are only as good as the metrics used to measure them. Yet many teams still rely on outdated or misleading measures like MAPE. This book is the first comprehensive, practitioner-friendly guide dedicated entirely to forecast evaluation metrics — blending clear theory, Python recipes, and real-world case studies.
Learn how to avoid common pitfalls, measure bias, handle intermittent demand, and apply advanced metrics like MASE, RMSSE, CRPS, pinball loss, and calibration scores. Each chapter includes formulas, code, and visuals to make concepts easy to apply.
Perfect for data scientists, ML engineers, analysts, researchers, and industry professionals in retail, finance, and energy. No heavy math required.
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Valery Manokhin
Valery Manokhin (PhD, MBA, CQF) is a data scienstist, machine learning researcher and book author. He earned his PhD in ML at Royal Holloway, University of London, under Prof. Vladimir Vovk, the creator of Conformal Prediction, and holds an MBA from Warwick and an MSc in Computational Statistics & Machine Learning from UCL.
His technical books — Mastering Modern Time Series Forecasting and Applied Conformal Prediction among them — are used by data scientists, ML engineers, and researchers in over 100+ countries.
In parallel, he produces English editions of classic Russian mathematics textbooks, beginning with Kiselev’s Arithmetic and now Algebra, Part I. The aim is straightforward: give English-speaking students access to the books that shaped generations of Russian mathematicians, in editions that match the originals’ rigour.
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