Next Article in Journal
Numerical Inverse Transformation Methods for Z-Transform
Next Article in Special Issue
A Novel Technique to Solve the Fuzzy System of Equations
Previous Article in Journal
Re-Evaluating the Classical Falling Body Problem
Previous Article in Special Issue
On Bipolar Fuzzy Gradation of Openness
Open AccessArticle

An Evolving Partial Consensus Fuzzy Collaborative Forecasting Approach

1
Department of Industrial Engineering and Management, National Chiao Tung University, 1001, University Road, Hsinchu 300, Taiwan
2
Department of Aeronautical Engineering, Chaoyang University of Technology, Taichung 41349, Taiwan
*
Author to whom correspondence should be addressed.
Mathematics 2020, 8(4), 554; https://doi.org/10.3390/math8040554
Received: 7 March 2020 / Revised: 8 April 2020 / Accepted: 8 April 2020 / Published: 10 April 2020
(This article belongs to the Special Issue Fuzzy Sets, Fuzzy Logic and Their Applications 2020)
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 observing partial consensus fuzzy intersection changes as the number of experts varies, including the appropriate number of experts. We applied the evolving partial-consensus fuzzy collaborative forecasting approach to forecasting dynamic random access memory product yield with real data. The proposed approach forecasting performance surpassed current fuzzy collaborative forecasting that considered overall consensus, and it increased forecasting accuracy 13% in terms of mean absolute percentage error. View Full-Text
Keywords: fuzzy collaborative forecasting; dynamic random access memory; partial consensus; fuzzy intersection fuzzy collaborative forecasting; dynamic random access memory; partial consensus; fuzzy intersection
Show Figures

Figure 1

MDPI and ACS Style

Chen, T.-C.T.; Wang, Y.-C.; Huang, C.-H. An Evolving Partial Consensus Fuzzy Collaborative Forecasting Approach. Mathematics 2020, 8, 554.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop