Business Forecasting : (Record no. 26915)
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| fixed length control field | 11404nam a22005653i 4500 |
| 001 - CONTROL NUMBER | |
| control field | EBC6579254 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | MiAaPQ |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240724115058.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS | |
| fixed length control field | m o d | |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
| fixed length control field | cr cnu|||||||| |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 240724s2021 xx o ||||0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781119782599 |
| Qualifying information | (electronic bk.) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| Canceled/invalid ISBN | 9781119782476 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (MiAaPQ)EBC6579254 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (Au-PeEL)EBL6579254 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (OCoLC)1250084654 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | MiAaPQ |
| Language of cataloging | eng |
| Description conventions | rda |
| -- | pn |
| Transcribing agency | MiAaPQ |
| Modifying agency | MiAaPQ |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | HD30.27 .B875 2021 |
| 082 0# - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 658.40355 |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Gilliland, Michael. |
| 245 10 - TITLE STATEMENT | |
| Title | Business Forecasting : |
| Remainder of title | The Emerging Role of Artificial Intelligence and Machine Learning. |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Newark : |
| Name of producer, publisher, distributor, manufacturer | John Wiley & Sons, Incorporated, |
| Date of production, publication, distribution, manufacture, or copyright notice | 2021. |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Date of production, publication, distribution, manufacture, or copyright notice | ©2021. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 1 online resource (435 pages) |
| 336 ## - CONTENT TYPE | |
| Content type term | text |
| Content type code | txt |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type term | computer |
| Media type code | c |
| Source | rdamedia |
| 338 ## - CARRIER TYPE | |
| Carrier type term | online resource |
| Carrier type code | cr |
| Source | rdacarrier |
| 490 1# - SERIES STATEMENT | |
| Series statement | Wiley and SAS Business Series |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Cover -- Title Page -- Copyright Page -- Contents -- Foreword -- Preface -- State of the Art -- Forecasting in Social Settings: The State of the Art* -- I. The Facts -- A Brief History of Forecasting -- When Predictions Go Wrong -- Improving Forecasting Accuracy over Time -- The Importance of Being Uncertain -- II. What We Know -- On Explaining the Past versus Predicting the Future -- On the (Non)existence of a Best Model -- On the Performance of Machine Learning -- III. What We Are Not Sure About -- On the Prediction of Recessions/Booms/Non-stable Environments -- On the Performances of Humans versus Models -- On the Value of Explanatory Variables -- IV. What We Don't Know -- On Thin/Fat Tails and Black Swans -- On Causality -- On Luck (and Other Factors) versus Skills -- V. Conclusions -- Notes -- References -- Chapter 1 Artificial Intelligence and Machine Learning in Forecasting -- 1.1 Deep Learning for Forecasting* -- Introduction -- What Is a Neural Network? -- How Do We Forecast with Neural Nets? -- Examples of Neural Forecasting Models -- References -- 1.2 Deep Learning for Forecasting: Current Trends and Challenges* -- Applying Neural Nets as Global Forecasting Models -- Pros and Cons of Neural Forecasting -- Current Trends and Challenges -- DL Software for Forecasting -- References -- 1.3 Neural Network-Based Forecasting Strategies* -- Introduction -- Neural Network Modeling in SAS Visual Forecasting -- Modeling Strategies -- Case Study: Ozone Prediction -- Case Study: Solar Energy Forecasting -- Best Practices and Other Tips -- Conclusion -- Acknowledgments -- References -- 1.4 Will Deep and Machine Learning Solve Our Forecasting Problems?* -- Introduction -- The Good and the Bad -- The Problems -- What about the M4 Competition? -- Conclusion -- References. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 1.5 Forecasting the Impact of Artificial Intelligence: The Emerging and Long-Term Future* -- Introduction -- The 10 Major Emerging Trends -- Supercomputers, High-Speed Networks, and the Cloud -- Medicine and Genomics -- Renewable Energy and Energy Storage -- AVs and Drones -- Greater Wealth and More Comfort -- The "Science Fiction" Type of Technological Developments -- Conclusions -- References -- 1.6 Forecasting the Impact of Artificial Intelligence: Another Voice* -- Forecasting AI's Computational Power -- AI's Impact on Employment -- Blockchain, IA, and Forecasting -- Intelligence Augmentation -- The Long-Term Future -- AI for Forecasting -- Summary -- 1.7 Smarter Supply Chains through AI* -- Introduction -- Supply-Chain Challenges -- Control-System Theory for Supply-Chain Management -- Applying AI/ML -- Lessons Learned -- References -- 1.8 Continual Learning: The Next Generation of Artificial Intelligence* -- Introduction -- Eliminating Our Own Complexities -- AutoML -- Continual Learning -- Continual Learning Augmentation -- Conclusion -- Acknowledgment -- References -- 1.9 Assisted Demand Planning Using Machine Learning* -- The Life of a Demand Planner -- What Is Forecast Value Added? -- Using Intelligent Automation to Improve a Demand Planner's FVA -- Conclusion -- References -- 1.10 Maximizing Forecast Value Add through Machine Learning and Behavioral Economics* -- Introduction -- Predicting Forecast Value Add -- Override Size -- Forecastability of Demand -- Override Classification Techniques -- Analysis -- Behavioral Economics: How to Influence Forecaster Behavior -- Summary -- References -- 1.11 The M4 Forecasting Competition - Takeaways for the Practitioner* -- M4 Background -- M4 Results -- Takeaways for Forecasting Practitioners -- Criticism of the M4 -- Looking Ahead to the M5 -- References -- Chapter 2 Big Data in Forecasting. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 2.1 Is Big Data the Silver Bullet for Supply-Chain Forecasting?* -- The "Big Data" Bubble -- Forecasting by Item or Consumer -- Big Data and Causal Forecasting -- Conclusion -- References -- 2.2 How Big Data Could Challenge Planning Processes across the Supply Chain* -- Introduction -- "Big Data" Sources and the Potential They Bring -- The Challenge for Aggregate and Detailed Planning -- Conclusions -- References -- Chapter 3 Forecasting Methods: Modeling, Selection, and Monitoring -- 3.1 Know Your Time Series* -- Data Availability -- Stationarity -- Forecastability and Scale -- Key Takeaways -- Note -- References -- 3.2 A Classification of Business Forecasting Problems* -- Introduction -- Dimensions of the Classification -- Strategic Forecasting -- Tactical Forecasting -- Operational Forecasting -- Publicly Available Data Sets -- Consequences: People, Skills, Methods, and Software -- References -- 3.3 Judgmental Model Selection* -- Forecasting with Judgment -- Exploring the Performance of Judgmental Model Selection -- The Behavioral Experiment -- Why Model-Build Works Better -- Implications for Software -- Final Comments -- References -- Commentary: A Surprisingly Useful Role for Judgment -- References -- Commentary: Algorithmic Aversion and Judgmental Wisdom -- References -- Commentary: Model Selection in Forecasting Software -- Reference -- Commentary: Exploit Information from the M4 Competition -- Reference -- 3.4 A Judgment on Judgment* -- Patterns That Aren't There -- Stories Trump Numbers -- The Perils of Imagination and Memory -- Chained to an Anchor -- A Pleasant Pension Surprise? -- Judging Lots of Possibilities -- References -- 3.5 Could These Recent Findings Improve Your Judgmental Forecasts?* -- Surprises -- Competition -- Combination -- Conclusions -- References -- 3.6 A Primer on Probabilistic Demand Planning*. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Moving to a Probabilistic Perspective of the Future -- The Differences between Statistical and Probabilistic Forecasts -- Planning with Probabilistic Forecasts -- 3.7 Benefits and Challenges of Corporate Prediction Markets* -- Introduction: Corporate Prediction Markets -- Forecast Accuracy -- Factors Influencing Accuracy -- Big Data - A Competitive Approach -- Concluding Thought -- References -- 3.8 Get Your CoV On . . .* -- Getting Your CoV On . . . -- Discussion on Demand -- A Side Discussion on MAPE and WMAPE -- 3.9 Standard Deviation Is Not the Way to Measure Volatility* -- Takeout -- 3.10 Monitoring Forecast Models Using Control Charts* -- Introduction -- Background -- Residual Analysis Methodology -- Illustrative Examples -- Summary -- References -- 3.11 Forecasting the Future of Retail Forecasting* -- Introduction -- Your Next Shopping Trip -- Digital Technologies and Trends -- Implications for the Retail Industry -- Implications for Retail Forecasting -- Conclusion -- References -- Commentary -- References -- Chapter 4 Forecasting Performance -- 4.1 Using Error Analysis to Improve Forecast Performance* -- Preview and Key Points from the Author -- Key Concepts -- Error Analysis -- Drivers of Forecast Quality -- Lessons Learned -- Conclusion -- References -- 4.2 Guidelines for Selecting a Forecast Metric* -- Idiosyncracies about the Measurement of Forecast Error -- Forecast Error Is a Funny Thing -- Lies, Damn Lies, and Statistics -- 4.3 The Quest for a Better Forecast Error Metric: Measuring More Than the Average Error* -- Introduction -- Point Forecasts vs. Probabilistic Forecasts -- Uncertainty and Inventory -- Percentile Error Metrics -- Conclusions -- Appendixes -- References -- 4.4 Beware of Standard Prediction Intervals from Causal Models* -- Introduction: Standard Prediction Intervals for a Regression Model Forecast. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Sources of Error in the Standard Prediction Interval -- Sources of Error Not Accounted For -- A Case Study -- The Derivation of the Inflation Factors -- Simulation -- Conclusions -- Extensions -- References -- Chapter 5 Forecasting Process: Communication, Accountability, and S& -- OP -- 5.1 Not Storytellers But Reporters* -- News and Evidence -- Not Storytellers But Reporters -- The Duty of Clarity -- Reference -- 5.2 Why Is It So Hard to Hold Anyone Accountable for the Sales Forecast?* -- Responsibility versus Accountability -- Are You Forecasting or Sales Planning? -- Are Responsibilities and Overall Accountability Well Defined? -- Appropriate Metrics for Sales Planning Process Monitoring -- Changes to the Game -- Who's "On First" in Your Company? The Checklist -- Conclusion -- 5.3 Communicating the Forecast: Providing Decision Makers with Insights* -- Asking Decision Makers What They Need -- Storyboard Structure -- Risk Management -- Conclusion -- References -- 5.4 An S& -- OP Communication Plan: The Final Step in Support of Company Strategy* -- Introduction -- Communications to Support Business Strategy -- S& -- OP and Strategy Execution -- The S& -- OP Communication Plan -- Summary -- References -- 5.5 Communicating Forecasts to the C-Suite: A Six-Step Survival Guide* -- Six Tips for Explaining the Forecast to Execs -- Introduction -- Articulate What the CFO Needs to Believe to Use the Forecast -- Accountants and Statisticians Think Differently about Data -- Don't Talk about Complex Diagnostic Statistics -- Expect a Skew in Consensus Forecasts -- Explain Sensitivities to Changes in Independent Variables as Thumb Rules, Not Coefficients -- Your Forecast Will Be Wrong - Be Ready to Explain If Error Looks More Likely to the Upside or Downside -- References -- 5.6 How to Identify and Communicate Downturns in Your Business*. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Part 1: Forecasting Heroes Catch Turning Points. |
| 588 ## - SOURCE OF DESCRIPTION NOTE | |
| Source of description note | Description based on publisher supplied metadata and other sources. |
| 590 ## - LOCAL NOTE (RLIN) | |
| Local note | Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Business forecasting. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Artificial intelligence. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Machine learning. |
| 655 #4 - INDEX TERM--GENRE/FORM | |
| Genre/form data or focus term | Electronic books. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Tashman, Len. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Sglavo, Udo. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Makridakis, Spyros G. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Petropoulos, Fotios. |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Relationship information | Print version: |
| Main entry heading | Gilliland, Michael |
| Title | Business Forecasting |
| Place, publisher, and date of publication | Newark : John Wiley & Sons, Incorporated,c2021 |
| International Standard Book Number | 9781119782476 |
| 797 2# - LOCAL ADDED ENTRY--CORPORATE NAME (RLIN) | |
| Corporate name or jurisdiction name as entry element | ProQuest (Firm) |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| Uniform title | Wiley and SAS Business Series |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://ebookcentral.proquest.com/lib/orpp/detail.action?docID=6579254">https://ebookcentral.proquest.com/lib/orpp/detail.action?docID=6579254</a> |
| Public note | Click to View |
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