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Relevant bibliographies by topics / Consensus forecasting

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Academic literature on the topic 'Consensus forecasting'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "Consensus forecasting"

1

Chen, Tin-Chih Toly, Yu-Cheng Wang, and Chin-Hau Huang. "An Evolving Partial Consensus Fuzzy Collaborative Forecasting Approach." Mathematics 8, no. 4 (2020): 554. http://dx.doi.org/10.3390/math8040554.

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Current fuzzy collaborative forecasting methods have rarely considered how to determine the appropriate number of experts to optimize forecasting performance. Therefore, this study proposes an evolving partial-consensus fuzzy collaborative forecasting approach to address this issue. In the proposed approach, experts apply various fuzzy forecasting methods to forecast the same target, and the partial consensus fuzzy intersection operator, rather than the prevalent fuzzy intersection operator, is applied to aggregate the fuzzy forecasts by experts. Meaningful information can be determined by obs

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Lackman, Conway, and Charles Brandon. "Consensus Forecasting Using Relative Error Weights." Marketing Intelligence & Planning 12, no. 1 (1994): 37–41. http://dx.doi.org/10.1108/02634509410052649.

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Woodcock, Frank, and Chermelle Engel. "Operational Consensus Forecasts." Weather and Forecasting 20, no. 1 (2005): 101–11. http://dx.doi.org/10.1175/waf-831.1.

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Abstract The objective consensus forecasting (OCF) system is an automated operational forecasting system that adapts to underlying numerical model upgrades within 30 days and generally outperforms direct model output (DMO) and model output statistics (MOS) forecasts. It employs routinely available DMO and MOS guidance combined after bias correction using a mean absolute error (MAE)-weighted average algorithm. OCF generates twice-daily forecasts of screen-level temperature maxima and minima, ground-level temperature minima, evaporation, sunshine hours, and rainfall and its probability for day 0

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Petreski, Marjan, Blagica Petreski, Despina Tumanoska, et al. "The Size and Effects of Emigration and Remittances in the Western Balkans. A Forecasting Based on a Delphi Process." Südosteuropa 65, no. 4 (2018): 679–95. http://dx.doi.org/10.1515/soeu-2017-0044.

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Abstract This paper aims at forecasting the size and effects of remittances and emigration in Macedonia, Albania, Serbia, and Kosovo, using a qualitative forecasting method, a Delphi questionnaire. The authors examined consensus building within and between two groups of respondents: ten experts and twenty remittance receivers per country, in three subsequent rounds–two on the same group and a third cross-round. Consensual results suggest that remittances in the projected five-year period will increase in Macedonia and Serbia, and will reduce in Albania and Kosovo. With less consensus, the resu

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Gregory, Allan W., and James Yetman. "The evolution of consensus in macroeconomic forecasting." International Journal of Forecasting 20, no. 3 (2004): 461–73. http://dx.doi.org/10.1016/s0169-2070(03)00064-5.

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Moutinho, Luiz, and Stephen F. Witt. "Forecasting the Tourism Environment Using a Consensus Approach." Journal of Travel Research 33, no. 4 (1995): 46–50. http://dx.doi.org/10.1177/004728759503300407.

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Sampson, Charles R., James L. Franklin, John A. Knaff, and Mark DeMaria. "Experiments with a Simple Tropical Cyclone Intensity Consensus." Weather and Forecasting 23, no. 2 (2008): 304–12. http://dx.doi.org/10.1175/2007waf2007028.1.

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Abstract Consensus forecasts (forecasts created by combining output from individual forecasts) have become an integral part of operational tropical cyclone track forecasting. Consensus aids, which generally have lower average errors than individual models, benefit from the skill and independence of the consensus members, both of which are present in track forecasting, but are limited in intensity forecasting. This study conducts experiments with intensity forecast aids on 4 yr of data (2003–06). First, the skill of the models is assessed; then simple consensus computations are constructed for

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Modrak, Vladimir, and Petre Bosun. "Using the Delphi Method in Forecasting Tourism Activity." International Letters of Social and Humanistic Sciences 25 (March 2014): 66–72. http://dx.doi.org/10.18052/www.scipress.com/ilshs.25.66.

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Delphi method is used to obtain usually medium or long-term forecasts, which involves establishing a consensus from a panel of experts asked to make estimates about the evolution of the economical phenomenon. If it cannot be obtain the wished consensus, the opinions having a certain degree of divergence, it aims to reduce as much of this dispersion, without a direct influence on the panel of experts, using a framework in which their expert feedback drive to the conclusion of the study. Of course there are some advantages and disadvantages of this method and these are presented in the article c

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Calitz, C. B., and E. V. D. M. Smit. "Die kombinering van ekonomiese vooruitskattings in die Suid-Afrikaanse konteks." South African Journal of Business Management 20, no. 4 (1989): 178–83. http://dx.doi.org/10.4102/sajbm.v20i4.960.

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In the literature on forecasting, consensus has been reached about improved forecasting accuracy brought about by the combination of two or more forecasts for a given variable. No consensus, however, exists about the exact way in which the various forecasts in the combination should be weighed. The evidence points towards simple weighing schemes. The present study utilizes South African macro-economic forecasts published by seven forecasters on eight variables to evaluate the benefits to be gained from combining forecasts and to evaluate the relative accuracy of a number of combination schemes

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Smirnov, S. V., N. V. Kondrashov, and A. S. Kachur. "Macroeconomic forecasting and macroeconomic forecasts." Voprosy Ekonomiki, no. 2 (February 6, 2024): 23–48. http://dx.doi.org/10.32609/0042-8736-2024-2-23-48.

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The specificity of macroeconomic forecasts is determined not so much by the list of predicted indicators or by mathematical tools used, but by the unavoidable human factor, which often generates great difference between forecasts made by various professionals. In Russian-language literature, this psychological aspect of macroeconomic forecasting has not received any attention; our work is designed to fill this gap. As a source of statistical data we used the forecasts extracted from the quarterly Poll of Professional Forecasters (PPF), which began in the first quarter of 2000. An analysis of r

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Dissertations / Theses on the topic "Consensus forecasting"

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Hughes, James R. "A weighted consensus approach to tropical cyclone 96-H and 120-H track forecasting." Thesis, Monterey, Calif. : Naval Postgraduate School, 2007. http://bosun.nps.edu/uhtbin/hyperion.exe/07Mar%5FHughes.pdf.

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Brushett, Ben Amon. "Assessment of Metocean Forecast Data and Consensus Forecasting for Maritime Search and Rescue and Pollutant Response Applications." Thesis, Griffith University, 2015. http://hdl.handle.net/10072/367700.

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Effective prediction of objects drifting on the water surface is essential to successful maritime search and rescue (SAR) services, since a more accurate prediction of the object’s likely location results in a greater probability for the success of a SAR operation. SAR drift models, based on Lagrangian stochastic particle trajectory models, are frequently utilised for this task. More recently, metocean (meteorological and oceanographic) forecast data has been used as input to these models to provide the environmental forcing (due to winds and ocean currents) that the object may be subject to.

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Li, Yen-Chen, and 李演珍. "Consensus Forecasting Management:Demand Forecasting and Financial Planning." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/74155247885505143375.

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碩士<br>國立臺灣大學<br>商學組<br>99<br>A competitive advantage exists when the firm is able to deliver the same benefits as competitors but at a lower cost(Cost Advantage), or deliver benefits that exceed those of competing products(Differentiation Advantage). Thus, a competitive advantage enables the firm to create superior value for its customers and superior profits for itself. The firm’s resources and capabilities together form its distinctive competencies. These competencies enable innovation, efficiency, quality and customer responsiveness, all of which can be leveraged to create a cost advantage

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Wang, Yi-Wen, and 王怡文. "An Empirical Evaluation of Measures for Consensus Forecasting." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/04075503789101286740.

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碩士<br>國立交通大學<br>經營管理研究所<br>98<br>We use the mean, median, trimmed mean and Winsorized mean in this study to investigate which method is easy to use and understand in consensus forecast. The data which we employ from Consensus Economics, Inc. are monthly real GDP, inflation and unemployment rate of seven countries (Taiwan, China, Japan, the United States of America, United Kingdom, France and Germany). We use them to state the forecast bias and accuracy during the period 1995 to 2008 and then to compare which method is better in recession and non-recession years. We find that: First of all,

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Scheier, Johannes. "Rationalität und Qualität von Wirtschaftsprognosen." Thesis, 2015. http://hdl.handle.net/11858/00-1735-0000-0022-5FE8-0.

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Wirtschaftsprognosen sollen die Unsicherheit bezüglich der zukünftigen wirtschaftlichen Entwicklung mindern und Planungsprozesse von Regierungen und Unternehmen unterstützen. Empirische Studien bescheinigen ihnen jedoch in aller Regel ein unbefriedigendes Qualitätsniveau. Auf der Suche nach den Ursachen hat sich in Form der rationalen Erwartungsbildung eine zentrale Grundforderung an die Prognostiker herausgebildet. So müssten offensichtliche und systematische Fehler, wie bspw. regelmäßige Überschätzungen, mit der Zeit erkannt und abgestellt werden. Die erste Studie der Dissertation übt Kriti

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Books on the topic "Consensus forecasting"

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United States. General Accounting Office. Accounting and Financial Management Division, ed. Consensus revenue forecasting. The Office, 1992.

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Emerson, P. J. Consensus voting systems. (P.J. Emerson?), 1991.

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New York (State). Legislature. Senate. Committee on Finance. New York State 2010 economic revenue consensus forecasting conference. s.n., 2010.

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Yanagida, John F. An evaluation of consensus distribution analysis for spot commodity forecasting. Columbia University Business School, Center for the Study of Futures Markets, 1985.

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Emerson, P. J. The politics of consensus: For the resolution of conflict and reform of majorityrule. Peter Emerson, 1994.

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Noel, Watson, and Harvard Business School, eds. Managing functional biases in organizational forecasts: A case study of consensus forecasting in supply chain planning. 2nd ed. Harvard Business School, 2008.

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Noel, Watson, and Harvard Business School, eds. Managing functional biases in organizational forecasts: A case study of consensus forecasting in supply chain planning. Harvard Business School, 2007.

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8

Ziebart, David Allen. Evidence regarding divergence of analysts' forecasts of annual earnings per share: Does consensus increase as the forecast horizon declines? College of Commerce and Business Administration,University of Illinois at Urbana-Champaign, 1986.

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Ziebart, David Allen. Evidence regarding divergence of analysts' forecasts of annual earnings per share: Does consensus increase as the forecast horizon declines? College of Commerce and Business Administration,University of Illinois at Urbana-Champaign, 1986.

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10

Tangredi, Sam J. Futures of war: Toward a consensus view of the future security environment, 2010-2035. Alidade Press, 2008.

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Book chapters on the topic "Consensus forecasting"

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Decker, J. Winn, and Bruce D. McDonald. "Consensus Forecasting." In The Palgrave Handbook of Government Budget Forecasting. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18195-6_20.

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Tang, Ming, and Huchang Liao. "Multiple Criteria Group Decision-Making Based on Hesitant Fuzzy Linguistic Consensus Model for Fashion Sales Forecasting." In Artificial Intelligence on Fashion and Textiles. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99695-0_40.

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King, David, Nadeeshani Wanigarathna, Keith Jones, and Joseph Ofori-Kuragu. "A Systematic Literature Review to Identify a Methodological Approach for Use in the Modelling and Forecasting of Capital Expenditure of Hyperscale Data Centres." In CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality. Firenze University Press, 2023. http://dx.doi.org/10.36253/10.36253/979-12-215-0289-3.37.

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The theme of ‘Managing the digital transformation of the construction industry’ emphasises the importance of considering various dimensions of digitalisation and optimising the built environment. This review aims to present methodological approaches from existing literature that elucidate location-related factors impacting the capital cost of data centres. These findings facilitate adjustments to historical cost data when estimating total costs for new data centres. A systematic literature review method was employed to ensure an objective and comprehensive synthesis. In conjunction with Bayes'

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King, David, Nadeeshani Wanigarathna, Keith Jones, and Joseph Ofori-Kuragu. "A Systematic Literature Review to Identify a Methodological Approach for Use in the Modelling and Forecasting of Capital Expenditure of Hyperscale Data Centres." In CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality. Firenze University Press, 2023. http://dx.doi.org/10.36253/979-12-215-0289-3.37.

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The theme of ‘Managing the digital transformation of the construction industry’ emphasises the importance of considering various dimensions of digitalisation and optimising the built environment. This review aims to present methodological approaches from existing literature that elucidate location-related factors impacting the capital cost of data centres. These findings facilitate adjustments to historical cost data when estimating total costs for new data centres. A systematic literature review method was employed to ensure an objective and comprehensive synthesis. In conjunction with Bayes'

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"Consensus Forecasts." In Economic Forecasting: The State of the Art. Routledge, 2016. http://dx.doi.org/10.4324/9781315480695-15.

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Wong, John, and Carl Ekstrom. "Consensus Revenue Estimating in State Government." In Government Budget Forecasting. Auerbach Publications, 2008. http://dx.doi.org/10.1201/9781420045833.ch19.

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Vonasek, Joseph, and William Earle Klay. "Consensus Forecasting for Budgeting in Theory and Practice." In Government Budget Forecasting. Auerbach Publications, 2008. http://dx.doi.org/10.1201/9781420045833.ch16.

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"Consensus Forecasting for Budgeting in Th eory and Practice." In Government Budget Forecasting. Routledge, 2017. http://dx.doi.org/10.4324/9781315093444-25.

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Qiao, Yuhua. "Use of Consensus Revenue Forecasting in U.S. State Governments." In Government Budget Forecasting. Auerbach Publications, 2008. http://dx.doi.org/10.1201/9781420045833.ch17.

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"Use of Consensus Revenue Forecasting in U.S. State Governments." In Government Budget Forecasting. Routledge, 2017. http://dx.doi.org/10.4324/9781315093444-26.

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Conference papers on the topic "Consensus forecasting"

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Last, David. "Consensus forecasting of zero-day vulnerabilities for network security." In 2016 International Carnahan Conference on Security Technology (ICCST). IEEE, 2016. http://dx.doi.org/10.1109/ccst.2016.7815718.

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Miyoshi, Takahiro, and Shigeo Matsubara. "Dynamically Forming a Group of Human Forecasters and Machine Forecaster for Forecasting Economic Indicators." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/64.

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How can human forecasts and a machine forecast be combined in inflation forecast tasks? A machine-learning-based forecaster makes a forecast based on a statistical model constructed from past time-series data, while humans take varied information such as economic policies into account. Combination methods for different forecasts have been studied such as ensemble and consensus methods. These methods, however, always use the same manner of combination regardless of the situation (input), which makes it difficult to use the advantages of different types of forecasters. To overcome this drawback,

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Poranki, Srikanth, Nagen Nagarur, and Krishnaswami Srihari. "Mapping Growth of an Emerging Technology: A Case Study of Flexible Electronics." In ASME 2009 InterPACK Conference collocated with the ASME 2009 Summer Heat Transfer Conference and the ASME 2009 3rd International Conference on Energy Sustainability. ASMEDC, 2009. http://dx.doi.org/10.1115/interpack2009-89315.

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Technological innovation is viewed as one of the main economic multipliers. In high-technology sectors, with intense competition and short product life cycles, failure to detect an emerging technology could be devastating to both incumbent technology pursuers and innovators. Firms typically employ methods such as a Delphi method, Technology growth curves or technology road-mapping methods to identify and forecast growth. Some of these methods are based on a life cycle approach where a technology is expected to follow an S-curve and then become obsolete in due time, which is analogous to market

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Rafiee, Javad, Pallav Sarma, Fernando Gutierrez, et al. "Energy Transition Meets Digital Transformation: Design and Implementation of a Comprehensive Carbon Emissions Estimation and Forecasting Platform." In Offshore Technology Conference. OTC, 2022. http://dx.doi.org/10.4043/31747-ms.

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Abstract With growing worldwide consensus about the impacts of climate change, the oil and gas industry faces unprecedented pressure to redefine itself, minimize its carbon footprint, and ultimately move towards net zero emissions. It is therefore imperative that the industry can easily and accurately measure its carbon footprint, forecast impact on emissions from operational decisions, monitor key sources of emissions and ultimately minimize it via operational and other optimizations. This need is further accelerated by regulatory, investor, consumer, and market forces, in many cases making i

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Wijaya, Nur, and James Sheng. "Effects of Imbibition During Well Shut-In on Ultimate Shale Oil Recovery: A Numerical Study." In SPE Western Regional Meeting. SPE, 2021. http://dx.doi.org/10.2118/200875-ms.

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Abstract Shale wells are often shut-in after hydraulic fracturing is finished. Shut-in often lasts for an extended period in the perceived hope to improve the ultimate oil recovery. However, current literature does not show a strong consensus on whether shut-in will improve the ultimate oil recovery. Because of the delayed production, evaluating the benefits of shut-in in improving the ultimate oil recovery is crucial. Otherwise, shut-in would merely delay the production and harm the economic performance. This paper uses a numerical flow-geomechanical modeling approach to investigate the effec

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Mokiy, Vladimir, and Tatiana Lukyanova. "Modern Transdisciplinarity: Results of the Development of the Prime Cause and Initial Ideas." In InSITE 2022: Informing Science + IT Education Conferences InSITE 2022. Informing Science Institute, 2022. http://dx.doi.org/10.28945/4931.

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Aim/Purpose This paper focuses on systematizing and rethinking the conformity of modern transdisciplinarity with its prime cause and initial ideas. Background The difficulties of implementing transdisciplinarity into science and education are connected with the fact that its generally accepted definition, identification characteristics, and methodological features are still missing. In or-der to eliminate these disadvantages of transdisciplinarity, its prime cause and initial ideas had to be detected. It is also important to analyze the correspondence of the existing opinions about transdiscip

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Reports on the topic "Consensus forecasting"

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Hajdini, Ina. Mis-specified Forecasts and Myopia in an Estimated New Keynesian Model. Federal Reserve Bank of Cleveland, 2023. http://dx.doi.org/10.26509/frbc-wp-202203r.

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The paper considers a New Keynesian framework in which agents form expectations based on a combination of autoregressive mis-specified forecasts and myopia. The proposed expectations formation process is shown to be consistent with all three empirical facts on consensus inflation forecasts. However, while mis-specified forecasts can be both sufficient and necessary to match all three facts, myopia alone is neither. The paper then derives the general equilibrium solution consistent with the proposed expectations formation process and estimates the model with likelihood-based Bayesian methods, y

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