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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 Yet many teams still rely on outdated or misleading measures like MAPE.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.
Valery Manokhin, PhD, MBA, CQF is Senior Data Science and AI Leader with over a decade of experience driving transformative machine learning solutions across global enterprises. Recognized author and educator in machine learning, AI, advanced forecasting, uncertainty quantification, with a proven track record of aligning data strategies with business objectives to deliver significant, measurable business outcomes.