Change language
English Deutsch
Change currency
€ EUR £ GBP $ USD
Licensed Unlicensed Requires Authentication Published by De Gruyter 2021

9 Triple Exponential Smoothing

From the book Data Science for Supply Chain Forecasting

  • Nicolas Vandeput
https://doi.org/10.1515/9783110671124-009
© 2021 Walter de Gruyter GmbH, Berlin/Munich/Boston
From the book
Data Science for Supply Chain Forecasting
Data Science for Supply Chain Forecasting

Chapters in this book (39)

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
Downloaded on 5.6.2024 from https://www.degruyter.com/document/doi/10.1515/9783110671124-009/html
Scroll to top button