How to make plausibility-based forecasting more accurate

© 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...

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Main Authors:	Kongliang Zhu, Nantiworn Thianpaen, Vladik Kreinovich
Format:	Book Series
Published:	2018
Subjects:	Computer Science
Online Access:	https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85012273390&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/57132
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Institution:	Chiang Mai University

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record_format	dspace
spelling	th-cmuir.6653943832-571322018-09-05T03:35:20Z How to make plausibility-based forecasting more accurate Kongliang Zhu Nantiworn Thianpaen Vladik Kreinovich Computer Science © 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. 2018-09-05T03:35:20Z 2018-09-05T03:35:20Z 2017-02-01 Book Series 1860949X 2-s2.0-85012273390 10.1007/978-3-319-50742-2_7 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85012273390&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/57132
institution	Chiang Mai University
building	Chiang Mai University Library
country	Thailand
collection	CMU Intellectual Repository
topic	Computer Science
spellingShingle	Computer Science Kongliang Zhu Nantiworn Thianpaen Vladik Kreinovich How to make plausibility-based forecasting more accurate
description	© 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.
format	Book Series
author	Kongliang Zhu Nantiworn Thianpaen Vladik Kreinovich
author_facet	Kongliang Zhu Nantiworn Thianpaen Vladik Kreinovich
author_sort	Kongliang Zhu
title	How to make plausibility-based forecasting more accurate
title_short	How to make plausibility-based forecasting more accurate
title_full	How to make plausibility-based forecasting more accurate
title_fullStr	How to make plausibility-based forecasting more accurate
title_full_unstemmed	How to make plausibility-based forecasting more accurate
title_sort	how to make plausibility-based forecasting more accurate
publishDate	2018
url	https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85012273390&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/57132
_version_	1681424821601173504

How to make plausibility-based forecasting more accurate

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