Using GA-based Adaptive Grey Model for solving small data sets forecasting problems

Der-Chiang Li, Wu Kuo Lin

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

Abstract

The forecast of short-term time series data is of practical value when enterprises face global competition. However, to successfully make it is difficult because of the limited data size. Therefore, it is considered as a great challenge to improve the preciseness of predictions when dealing with such limited data. In decades, the Grey Model (GM) has significant developments in theories and applications in real world. However, the accuracy of GM can be improved in some ways, and one of these is to find the suitable background values. To achieve it, the Adaptive Grey Model was proposed by taking the occurring trend of data into consideration, and the experimental results demonstrated better preciseness than those of some other improved GM models. In fact, setting the suitable background values of GM can be treated as the process in searching the optimal solutions. This paper thus employs the genetic algorithm (GA) to achieve this by taking the parameters generated by AGM as the initial solutions to build a more accurate model, called GAAGM(1,1).

Original languageEnglish
Title of host publicationProceedings of 2013 IEEE International Conference on Grey Systems and Intelligent Services, GSIS 2013
Pages477-480
Number of pages4
DOIs
Publication statusPublished - 2013 Dec 1
Event2013 24th IEEE International Conference on Grey Systems and Intelligent Services, GSIS 2013 - Macau, China
Duration: 2013 Nov 152013 Nov 17

Publication series

NameProceedings of IEEE International Conference on Grey Systems and Intelligent Services, GSIS
ISSN (Print)2166-9430
ISSN (Electronic)2166-9449

Other

Other2013 24th IEEE International Conference on Grey Systems and Intelligent Services, GSIS 2013
CountryChina
CityMacau
Period13-11-1513-11-17

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Hardware and Architecture
  • Control and Systems Engineering

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