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Data Science for Supply Chain Forecasting 2nd Edition Paperback- 2021 by Nico...
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eBay item number:204188579375
Item specifics
- Condition
- Like New
- Seller Notes
- “This item looks like Brand new. satisfaction guaranteed.”
- Book Title
- Data Science for Supply Chain Forecasting Paperback- 2021
- Subject
- Sales & Selling / Management, Total Quality Management, Economics / General, Management
- Original Language
- English
- ISBN
- 3110671107
- Subject Area
- Business & Economics
- Publication Name
- Data Science for Supply Chain Forecasting
- Item Length
- 9.4 in
- Publisher
- DE Gruyter Gmbh, Walter
- Publication Year
- 2021
- Type
- Textbook
- Format
- Trade Paperback
- Language
- English
- Item Width
- 6.7 in
- Item Weight
- 18.5 Oz
- Number of Pages
- 310 Pages
About this product
Product Information
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 chains to achieve excellence in demand forecasting. This second edition adds more than 45 percent extra content with four new chapters including an introduction to neural networks and the forecast value added framework. Part I focuses on statistical "traditional" models, Part II, on machine learning, and the all-new Part III discusses demand forecasting process management. The various chapters focus on both forecast models and new concepts such as metrics, underfitting, overfitting, outliers, feature optimization, and external demand drivers. The book is replete with do-it-yourself sections with implementations provided in Python (and Excel for the statistical models) to show the readers how to apply these models themselves. This hands-on book, covering the entire range of forecasting-from the basics all the way to leading-edge models-will benefit supply chain practitioners, forecasters, and analysts looking to go the extra mile with demand forecasting.
Product Identifiers
Publisher
DE Gruyter Gmbh, Walter
ISBN-10
3110671107
ISBN-13
9783110671100
eBay Product ID (ePID)
8038594648
Product Key Features
Publication Name
Data Science for Supply Chain Forecasting
Format
Trade Paperback
Language
English
Subject
Sales & Selling / Management, Total Quality Management, Economics / General, Management
Publication Year
2021
Type
Textbook
Subject Area
Business & Economics
Number of Pages
310 Pages
Dimensions
Item Length
9.4 in
Item Width
6.7 in
Item Weight
18.5 Oz
Additional Product Features
LCCN
2020-951955
Lc Classification Number
Hd38.5
Grade from
College Graduate Student
Grade to
College Graduate Student
Reviews
"I had a chance to review the manuscript. It is a very good book. For the supply chain managers out there, you should read at least the first few chapters, and then have others on your team read the rest of it and act on it ... you can have close to state-of-the-art forecasts with a minimum of effort.... This book closes the coffin on vendors who are selling only a handful of forecasting models." --Joannes Vermorel, Founder and CEO, Lokad "The objective of Data Science for Supply Chain Forecasting is to show practitioners how to apply the statistical and ML models described in the book in simple and actionable 'do-it-yourself' ways by showing, first, how powerful the ML methods are, and second, how to implement them with minimal outside help, beyond the 'do-it-yourself' descriptions provided in the book." --Prof. Spyros Makridakis, Founder of the Makridakis Open Forecasting Center (MOFC) and organizer of the M competitions Institute For the Future (IFF), University of Nicosia "In an age where analytics and machine learning are taking on larger roles in business forecasting, Nicolas' book is perfect for professionals who want to understand how they can use technology to predict the future more reliably." -- Daniel Stanton, Author, Supply Chain Management for Dummies
Table of Content
I Statistical Forecast Moving Average Forecast Error Exponential Smoothing Underfitting Double Exponential Smoothing Model Optimization Double Smoothing with Damped Trend Overfitting Triple Exponential Smoothing Outliers Triple Additive Exponential smoothing II Machine Learning Machine Learning Tree Parameter Optimization Forest Feature Importance Extremely Randomized Trees Feature Optimization Adaptive Boosting Exogenous Information & Leading Indicators Extreme Gradient Boosting Categories Clustering Glossary
Copyright Date
2021
Target Audience
Scholarly & Professional
Dewey Decimal
658.70282
Dewey Edition
23
Illustrated
Yes
Item description from the seller
Seller assumes all responsibility for this listing.
eBay item number:204188579375
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