Data Science for Supply Chain Forecasting /

Data Science for Supply Chain Forecasting / / Nicolas Vandeput.

Using data science in order to solve a problem requires a scientific mindset more than coding skills. Data Science for Supply Chain Forecasting, Second Edition contends that a true scientific method which includes experimentation, observation, and constant questioning must be applied to supply chain...

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Bibliographic Details
Superior document:Title is part of eBook package: De Gruyter DG Ebook Package English 2021
VerfasserIn:
Vandeput, Nicolas,
MitwirkendeR:
Makridakis, Spyros,
Ndiaye, Alassane B.,
Place / Publishing House:Berlin ;, Boston : : De Gruyter, , [2021]
©2021
Year of Publication:2021
Edition:2nd ed.
Language:English
Online Access:
Physical Description:1 online resource (XXVIII, 282 p.)
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Table of Contents:
  • Frontmatter
  • Acknowledgments
  • About the Author
  • Foreword – Second Edition
  • Foreword – First Edition
  • Contents
  • Introduction
  • Part I: Statistical Forecasting
  • 1 Moving Average
  • 2 Forecast KPI
  • 3 Exponential Smoothing
  • 4 Underfitting
  • 5 Double Exponential Smoothing
  • 6 Model Optimization
  • 7 Double Smoothing with Damped Trend
  • 8 Overfitting
  • 9 Triple Exponential Smoothing
  • 10 Outliers
  • 11 Triple Additive Exponential Smoothing
  • Part II: Machine Learning
  • 12 Machine Learning
  • 13 Tree
  • 14 Parameter Optimization
  • 15 Forest
  • 16 Feature Importance
  • 17 Extremely Randomized Trees
  • 18 Feature Optimization #1
  • 19 Adaptive Boosting
  • 20 Demand Drivers and Leading Indicators
  • 21 Extreme Gradient Boosting
  • 22 Categorical Features
  • 23 Clustering
  • 24 Feature Optimization #2
  • 25 Neural Networks
  • Part III: Data-Driven Forecasting Process Management
  • 26 Judgmental Forecasts
  • 27 Forecast Value Added
  • Now It’s Your Turn!
  • A Python
  • Bibliography
  • Glossary
  • Index

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