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15 records from EconBiz based on author Name
1. Forecasting : theory and practice
Petropoulos, Fotios; Apiletti, Daniele; Assimakopoulos, V.; Babai, M. Zied; Barrow, Devon K.; Ben Taieb, Souhaib; Bergmeir, Christoph; Bessa, Ricardo J.; Bijak, Jakub; Boylan, John E.; Browell, Jethro; Carnevale, Claudio; Castle, Jennifer; Cirillo, Pasquale; Clements, Michael P.; Cordeiro, Clara; Oliveira, Fernando Luiz Cyrino; Baets, Shari de; Dokumentov, Alexander; Ellison, Joanne; Fiszeder, Piotr; Franses, Philip Hans; Frazier, David T.; Gilliland, Michael; Gönül, M. Sinan; Goodwin, Paul; Grossi, Luigi; Grushka-Cockayne, Yael; Guidolin, Mariangela; Guidolin, Massimo; Gunter, Ulrich; Guo, Xiaojia; Guseo, Renato; Harvey, Nigel; Hendry, David F.; Hollyman, Ross; Januschowski, Tim; Jeon, Jooyoung; Jose, Victor Richmond R.; Kang, Yanfei; Koehler, Anne B.; Kolassa, Stephan; Kourentzes, Nikolaos; Leva, Sonia; Li, Feng; Litsiou, Konstantia; Makridakis, Spyros G.; Martin, Gael M.; Martinez, Andrew B.; Meeran, Sheik; Modis, Theodore; Nikolopoulos, Konstantinos; Önkal, Dilek; Paccagnini, Alessia; Panagiotelis, Anastasios; Panapakidis, Ioannis; Pavia, José Manuel; Pedio, Manuela; Pedregal, Diego J.; Pinson, Pierre; Ramos, Patrícia; Rapach, David E.; Reade, J. James; Rostami-Tabar, Bahman; Rubaszek, Michał; Sermpinis, Georgios; Shang, Han Lin; Spiliotis, Evangelos; Syntetos, Aris A.; Talagala, Priyanga Dilini; Talagala, Thiyanga S.; Tashman, Len; Thomakos, Dimitrios D.; Thorarinsdottir, Thordis; Todini, Ezio; Trapero Arenas, Juan Ramón; Wang, Xiaoqian; Winkler, Robert L.; Yusupova, Alisa; Ziel, Florian;2022
Type: Aufsatz in Zeitschrift; Article in journal;
Availability: Link
Citations: 163 (based on OpenCitations)
2. Business forecasting : the emerging role of artificial intelligence and machine learning
abstract"Forecasting is the process of making predictions of the future based on past and present data and most commonly by analysis of trends. A commonplace example might be estimation of some variable of interest at some specified future date. Prediction is a similar, but more general term This book provides ideas from the most important and influential authors in the field of forecasting on an array of topics that are highly relevant. It provides multiple perspectives on relevant issues like monitoring forecast performance, forecasting process, communication and accountability for the forecast, the use of big data in forecasting, and the role of AI/ML in enhancing traditional time series forecasting methods. Note: Content is mostly material previously published in "practitioner" journals (Foresight and Journal of Business Forecasting), with a few articles from the academic International Journal of Forecasting. Some articles report on academic research, or include case studies, but most are thoughtful discussion of important business forecasting topics, such as the role of the sales force in forecasting, or the value of judgmental overrides to a statistical forecast, or how to determine what forecast error is "avoidable." Articles were chosen for their importance, influence, informativeness, and for being provocative -- leading the reader to new considerations and ideas"--
Gilliland, Michael; Tashman, Len; Sglavo, Udo;2021
Type: Aufsatzsammlung; Beiträge
3. Business Forecasting : The Emerging Role of Artificial Intelligence and Machine Learning
abstractCover -- 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.
Gilliland, Michael; Tashman, Len; Sglavo, Udo;2021
Type: Aufsatzsammlung; Beiträge
Availability: Link
4. Business forecasting : practical problems and solutions
abstract"This title provides many of the most important and though-provoking articles by the leading business forecasting practitioners and academics. It exposes the reader to many of the best minds (and most provocative ideas) in the forecasting profession, with thorough referencing to related material for further reading. It provides: - A critical look at many of the vexing problems in business forecasting, such as volatility, forecastability, performance metrics, and human interaction in the forecasting process. - Introduces emerging new approaches such as combining data mining with forecasting and aggregating/reconciling across time hierarchies. - Addresses the often overlooked topic of data preparation and data quality (part of the "pre-processing" of data prior to forecasting. - Covers the proven (yet rarely used) method of combining forecasts to improve accuracy. Contains a mix of more formal/rigorous pieces, with brief chapters (adapted from blog posts) dealing narrowly with very specific topics"--
Gilliland, Michael; Tashman, Len; Sglavo, Udo;2015
Type: Ratgeber; Guidebook; Sammelwerk; Collection of articles of several authors;
5. Business forecasting : practical problems and solutions
abstractIntro -- Praise -- Series -- Title page -- Copyright -- Foreword -- Preface -- Chapter 1 Fundamental Considerations in Business Forecasting -- 1.1 Getting Real about Uncertainty -- 1.2 What Demand Planners Can Learn from the Stock Market -- 1.3 Toward a More Precise Definition of Forecastability -- 1.4 Forecastablity: A New Method for Benchmarking and Driving Improvement -- 1.5 Forecast Errors and Their Avoidability -- 1.6 The Perils of Benchmarking -- 1.7 Can We Obtain Valid Benchmarks from Published Surveys of Forecast Accuracy? -- 1.8 Defining "Demand" for Demand Forecasting -- 1.9 Using Forecasting to Steer the Business: Six Principles -- 1.10 The Beauty of Forecasting -- Chapter 2 Methods of Statistical Forecasting -- 2.1 Confessions of a Pragmatic Forecaster -- 2.2 New Evidence on the Value of Combining Forecasts -- 2.3 How to Forecast Data Containing Outliers -- 2.4 Selecting Your Statistical Forecasting Level -- 2.5 When Is a Flat-line Forecast Appropriate? -- 2.6 Forecasting by Time Compression -- 2.7 Data Mining for Forecasting: An Introduction -- 2.8 Process and Methods for Data Mining for Forecasting -- 2.9 Worst-Case Scenarios in Forecasting: How Bad Can Things Get? -- 2.10 Good Patterns, Bad Patterns -- Chapter 3 Forecasting Performance Evaluation and Reporting -- 3.1 Dos and Don'ts of Forecast Accuracy Measurement: A Tutorial -- 3.2 How to Track Forecast Accuracy to Guide Forecast Process Improvement -- 3.3 A "Softer" Approach to the Measurement of Forecast Accuracy -- 3.4 Measuring Forecast Accuracy -- 3.5 Should We Define Forecast Error as e = F - A or e = A - F? -- 3.6 Percentage Error: What Denominator? -- 3.7 Percentage Errors Can Ruin Your Day -- 3.8 Another Look at Forecast-Accuracy Metrics for Intermittent Demand -- 3.9 Advantages of the MAD/Mean Ratio over the MAPE.
Gilliland, Michael; Tashman, Len; Sglavo, Udo;2015
Type: Aufsatzsammlung; Beiträge
Availability: Link
6. Forecaster in the Field: Interview with Clive Jones
Tashman, Len;2015
Availability: Link
7. Interview with Fotios Petropoulos
Tashman, Foresight Editor Len;2015
Availability: Link
8. ARIMA: The Models of Box and Jenkins
Stellwagen, Eric; Tashman, Len;2013
Availability: Link
9. The Forecasting Process: Guiding Principles, Preview to the Commentaries
Tashman, Len;2012
Availability: Link
10. Outrageous Fortunes: How Daniel Altman Sees the Future of the Global Economy
Tashman, Len;2012
Availability: Link