Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/57132
Title: How to make plausibility-based forecasting more accurate
Authors: Kongliang Zhu
Nantiworn Thianpaen
Vladik Kreinovich
Keywords: Computer Science
Issue Date: 1-Feb-2017
Abstract: © Springer International Publishing AG 2017. In recent papers, a new plausibility-based forecasting method was proposed. While this method has been empirically successful, one of its steps—selecting a uniform probability distribution for the plausibility level—is heuristic. It is therefore desirable to check whether this selection is optimal or whether a modified selection would like to a more accurate forecast. In this paper, we show that the uniform distribution does not always lead to (asymptotically) optimal estimates, and we show how to modify the uniform-distribution step so that the resulting estimates become asymptotically optimal.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85012273390&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/57132
ISSN: 1860949X
Appears in Collections:CMUL: Journal Articles

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