A novel composition forecasting model based on choquet integral with respect to L-measure and O-density

Hsiang Chuan Liu, Bai Cheng Jeng, Yen Kuei Yu, Shang Ling Ou, Yih Chang Ou, Hsien Chang Tsai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper, based on L-measure and O-density, a novel composition forecasting model is proposed. For evaluating this new composition forecasting model, a real data experiment by using the sequential mean square error was conducted. Based on O-density, the performances of Choquet integral composition forecasting model with the L-measure, Lambda-measure and P-measure, respectively, a ridge regression composition forecasting model and a multiple linear regression composition forecasting model and the traditional linear weighted composition forecasting model were compared. Experimental results show that the Choquet integral composition forecasting model with respect to the L-measure outperforms other 5 composition forecasting models.

Original languageEnglish
Title of host publicationAdvanced Manufacturing Technology
PublisherTrans Tech Publications Ltd
Pages1245-1248
Number of pages4
ISBN (Print)9783037853702
DOIs
Publication statusPublished - 2012 Jan 1
Event3rd international Conference on Manufacturing Science and Engineering, ICMSE 2012 - Xiamen, China
Duration: 2012 Mar 272012 Mar 29

Publication series

NameAdvanced Materials Research
Volume472-475
ISSN (Print)1022-6680

Other

Other3rd international Conference on Manufacturing Science and Engineering, ICMSE 2012
CountryChina
CityXiamen
Period12-03-2712-03-29

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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    Liu, H. C., Jeng, B. C., Yu, Y. K., Ou, S. L., Ou, Y. C., & Tsai, H. C. (2012). A novel composition forecasting model based on choquet integral with respect to L-measure and O-density. In Advanced Manufacturing Technology (pp. 1245-1248). (Advanced Materials Research; Vol. 472-475). Trans Tech Publications Ltd. https://doi.org/10.4028/www.scientific.net/amr.472-475.1245