Lessons from Central Forecasting PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Lessons from Central Forecasting PDF full book. Access full book title Lessons from Central Forecasting by Duncan Burn. Download full books in PDF and EPUB format.
Author: Duncan Burn
Publisher:
ISBN:
Category :
Languages : en
Pages : 62
Get Book Here
Book Description
Author: Duncan Burn
Publisher:
ISBN:
Category :
Languages : en
Pages : 62
Get Book Here
Book Description
Author: Duncan Lyall Burn
Publisher:
ISBN:
Category : Economic forecasting
Languages : en
Pages : 76
Get Book Here
Book Description
Author: Duncan Burn
Publisher:
ISBN: 9780255695138
Category :
Languages : en
Pages :
Get Book Here
Book Description
Author: Michael Gilliland
Publisher: John Wiley & Sons
ISBN: 1119782473
Category : Business & Economics
Languages : en
Pages : 435
Get Book Here
Book Description
Discover the role of machine learning and artificial intelligence in business forecasting from some of the brightest minds in the field In Business Forecasting: The Emerging Role of Artificial Intelligence and Machine Learning accomplished authors Michael Gilliland, Len Tashman, and Udo Sglavo deliver relevant and timely insights from some of the most important and influential authors in the field of forecasting. You'll learn about the role played by machine learning and AI in the forecasting process and discover brand-new research, case studies, and thoughtful discussions covering an array of practical topics. The book offers multiple perspectives on issues like monitoring forecast performance, forecasting process, communication and accountability for forecasts, and the use of big data in forecasting. You will find: Discussions on deep learning in forecasting, including current trends and challenges Explorations of neural network-based forecasting strategies A treatment of the future of artificial intelligence in business forecasting Analyses of forecasting methods, including modeling, selection, and monitoring In addition to the Foreword by renowned researchers Spyros Makridakis and Fotios Petropoulos, the book also includes 16 "opinion/editorial" Afterwords by a diverse range of top academics, consultants, vendors, and industry practitioners, each providing their own unique vision of the issues, current state, and future direction of business forecasting. Perfect for financial controllers, chief financial officers, business analysts, forecast analysts, and demand planners, Business Forecasting will also earn a place in the libraries of other executives and managers who seek a one-stop resource to help them critically assess and improve their own organization's forecasting efforts.
Author: Philip E. Tetlock
Publisher: Crown
ISBN: 080413670X
Category : Business & Economics
Languages : en
Pages : 331
Get Book Here
Book Description
NEW YORK TIMES BESTSELLER • NAMED ONE OF THE BEST BOOKS OF THE YEAR BY THE ECONOMIST “The most important book on decision making since Daniel Kahneman's Thinking, Fast and Slow.”—Jason Zweig, The Wall Street Journal Everyone would benefit from seeing further into the future, whether buying stocks, crafting policy, launching a new product, or simply planning the week’s meals. Unfortunately, people tend to be terrible forecasters. As Wharton professor Philip Tetlock showed in a landmark 2005 study, even experts’ predictions are only slightly better than chance. However, an important and underreported conclusion of that study was that some experts do have real foresight, and Tetlock has spent the past decade trying to figure out why. What makes some people so good? And can this talent be taught? In Superforecasting, Tetlock and coauthor Dan Gardner offer a masterwork on prediction, drawing on decades of research and the results of a massive, government-funded forecasting tournament. The Good Judgment Project involves tens of thousands of ordinary people—including a Brooklyn filmmaker, a retired pipe installer, and a former ballroom dancer—who set out to forecast global events. Some of the volunteers have turned out to be astonishingly good. They’ve beaten other benchmarks, competitors, and prediction markets. They’ve even beaten the collective judgment of intelligence analysts with access to classified information. They are "superforecasters." In this groundbreaking and accessible book, Tetlock and Gardner show us how we can learn from this elite group. Weaving together stories of forecasting successes (the raid on Osama bin Laden’s compound) and failures (the Bay of Pigs) and interviews with a range of high-level decision makers, from David Petraeus to Robert Rubin, they show that good forecasting doesn’t require powerful computers or arcane methods. It involves gathering evidence from a variety of sources, thinking probabilistically, working in teams, keeping score, and being willing to admit error and change course. Superforecasting offers the first demonstrably effective way to improve our ability to predict the future—whether in business, finance, politics, international affairs, or daily life—and is destined to become a modern classic.
![Lessons from Central Forecasting: Three Essays on the Techniques and Fallibility of Statistical Measurement and Projection in Steel, Doctors and Social Insurance [by] Duncan Burn, J. R. Seale, A. R. N. Ratcliff PDF](https://books.google.com/books/content/images/frontcover/TBnJwgEACAAJ?fife=w80-h100)
Author: Duncan Lyall Burn
Publisher:
ISBN:
Category : Economic forecasting
Languages : en
Pages : 62
Get Book Here
Book Description
Author: Mohsen Hamoudia
Publisher: Springer Nature
ISBN: 3031358791
Category : Business & Economics
Languages : en
Pages : 441
Get Book Here
Book Description
This book is a comprehensive guide that explores the intersection of artificial intelligence and forecasting, providing the latest insights and trends in this rapidly evolving field. The book contains fourteen chapters covering a wide range of topics, including the concept of AI, its impact on economic decision-making, traditional and machine learning-based forecasting methods, challenges in demand forecasting, global forecasting models, meta-learning and feature-based forecasting, ensembling, deep learning, scalability in industrial and optimization applications, and forecasting performance evaluation. With key illustrations, state-of-the-art implementations, best practices, and notable advances, this book offers practical insights into the theory and practice of AI-based forecasting. This book is a valuable resource for anyone involved in forecasting, including forecasters, statisticians, data scientists, business analysts, or decision-makers.
Author: Nicolas Vandeput
Publisher: Simon and Schuster
ISBN: 163835197X
Category : Computers
Languages : en
Pages : 292
Get Book Here
Book Description
Lead your demand planning process to excellence and deliver real value to your supply chain. In Demand Forecasting Best Practices you’ll learn how to: Lead your team to improve quality while reducing workload Properly define the objectives and granularity of your demand planning Use intelligent KPIs to track accuracy and bias Identify areas for process improvement Help planners and stakeholders add value Determine relevant data to collect and how best to collect it Utilize different statistical and machine learning models An expert demand forecaster can help an organization avoid overproduction, reduce waste, and optimize inventory levels for a real competitive advantage. Demand Forecasting Best Practices teaches you how to become that virtuoso demand forecaster. This one-of-a-kind guide reveals forecasting tools, metrics, models, and stakeholder management techniques for delivering more effective supply chains. Everything you learn has been proven and tested in a live business environment. Discover author Nicolas Vandeput’s original five step framework for demand planning excellence and learn how to tailor it to your own company’s needs. Illustrations and real-world examples make each concept easy to understand and easy to follow. You’ll soon be delivering accurate predictions that are driving major business value. About the Technology An expert demand forecaster can help an organization avoid overproduction, reduce waste, and optimize inventory levels for a real competitive advantage. This book teaches you how to become that virtuoso demand forecaster. About the Book Demand Forecasting Best Practices reveals forecasting tools, metrics, models, and stakeholder management techniques for managing your demand planning process efficiently and effectively. Everything you learn has been proven and tested in a live business environment. Discover author Nicolas Vandeput’s original five step framework for demand planning excellence and learn how to tailor it to your own company’s needs. Illustrations and real-world examples make each concept easy to understand and easy to follow. You’ll soon be delivering accurate predictions that are driving major business value. What's Inside Enhance forecasting quality while reducing team workload Utilize intelligent KPIs to track accuracy and bias Identify process areas for improvement Assist stakeholders in sales, marketing, and finance Optimize statistical and machine learning models About the Reader For demand planners, sales and operations managers, supply chain leaders, and data scientists. About the Author Nicolas Vandeput is a supply chain data scientist, the founder of consultancy company SupChains in 2016, and a teacher at CentraleSupélec, France. Table of Contents: Part 1 - Forecasting demand 1 Demand forecasting excellence 2 Introduction to demand forecasting 3 Capturing unconstrained demand (and not sales) 4 Collaboration: data sharing and planning alignment 5 Forecasting hierarchies 6 How long should the forecasting horizon be? 7 Should we reconcile forecasts to align supply chains? Part 2 - Measuring forecasting quality 8 Forecasting metrics 9 Choosing the best forecasting KPI 10 What is a good forecast error? 11 Measuring forecasting accuracy on a product portfolio Part 3 - Data-driven forecasting process 12 Forecast value added 13 What do you review? ABC XYZ segmentations and other methods Part 4 - Forecasting methods 14 Statistical forecasting 15 Machine learning 16 Judgmental forecasting 17 Now it’s your turn!
Author: David F. Hendry
Publisher:
ISBN:
Category : Economic forecasting
Languages : en
Pages : 37
Get Book Here
Book Description
Author: Antje Artmann
Publisher: GRIN Verlag
ISBN: 3638127958
Category : Business & Economics
Languages : en
Pages : 12
Get Book Here
Book Description
Seminar paper from the year 2001 in the subject Business economics - Investment and Finance, grade: 9, Maastricht University (Economics and Sociology Faculty), language: English, abstract: In all aspects of our daily live, we seek to anticipate or forecast events. Especially organizations and companies are engaged in producing and using a full range of different economic forecasts. The widespread usefulness and application of forecasting systems and statistical and econometric modeling techniques has become solidly entrenched. Being aware of this fact, has led to a fundamental need for better quantitative analysis and business planning. Private and public sectors alike have found it both practical and essential to employ more rigorous analytical framework. Accordingly, more sophisticated forecasting techniques to enhance the level of predictability and confidence are required to foresee future events. The need for such forecasts arises because people are taking positions and enter into commitments about the future. Therefore, a need to form a view about the possible future consequences of these positions or commitments exists. Thus, in economic and business life, forecasts are essential, and errors can be very costly. According to those facts, now the question arises: What factors influence the accuracy if forecasts? In the following paper, three different forecasting methods will be explained and evaluated according to their accuracy. There exist diverse techniques of forecasting; those methods may be either formal or intuitive. Nevertheless, as the future is unknown, all forecasting systems rest ultimately on learning from the past. There exist naïve processes extrapolating the past in a simple way. But those will be prone to error when the world changes. More sophisticated methods seek to foresee change by understanding the source of past changes, and therefore incorporate change in the forecast. The standard output from macro models is a central forecast, that is, a prediction of the most likely path for the variables of interest. But these central forecasts are subject to appreciable uncertainty, and this needs to be taken into account in using them. One way to do so is to associate with the central forecasts an estimate of their possible error.
