Title
An envelopment learning procedure for improving prediction accuracies of grey models
Abstract
•An envelopment procedure is developed for improving accuracy of GM and its extents.•A process of creating fuzzy short-term time series (FSTTS) data is proposed.•GM and its extents are adopted to construct predictive models with the FSTTS data.•A defuzzifying process is proposed for aggregating predictions of the GM models.•A forecasting task of customer demand in an IC-assembly company is revealed.•The experimental results demonstrate the effectiveness of the proposed method.
Year
DOI
Venue
2020
10.1016/j.cie.2019.106185
Computers & Industrial Engineering
Keywords
Field
DocType
Short term time series data,Fuzzy time series,Grey models
Data mining,Time series,Mathematical optimization,Fuzzy logic,Envelopment,Engineering,Gray (horse)
Journal
Volume
ISSN
Citations 
139
0360-8352
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Chien-Chih Chen111120.42
Che-Jung Chang2365.62
Zheng-Yun Zhuang300.34
Der-Chiang Li400.34