Employing GA-based Adaptive Grey Model for learning with short-term sequence data

Der Chiang Li, Wu Kuo Lin

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Product lifecycles are becoming shorter and shorter because of the global competition. In such highly competitive condition, the importance for enterprises to build forecasting models with short-term time series data significantly arises in recent decade. Unfortunately, since the information the short-term data provides is limited, it is a great challenge to learn its pattern behavior. To overcome the issue, the Grey Model (GM) reveals a clear direction by accumulating data values to make them represent as a monotonically increasing sequence. Accordingly, improving the forecasting accuracy of GM has remarkable developments in theories in decades. Among them, the Adaptive Grey Model (AGM) tries to find the suitable background values by means of taking the data occurring trend into consideration. In fact, setting the suitable background values of GM can be regarded as a process of searching the optimal solutions. This paper thus employs the genetic algorithm (GA) to facilitate the improvement of searching process, where the initial solutions are obtained from AGM, to build a more accurate model named as GAAGM(1.1). The experiment results show that GAAGM outperforms AGM and some other improved GM models in prediction accuracy; and furthermore, this demonstrates that searching for more suitable coefficient sets can indeed help enhance the theory structure of GM models.

Original languageEnglish
Pages (from-to)96-106
Number of pages11
JournalJournal of Grey System
Issue number4
Publication statusPublished - 2013

All Science Journal Classification (ASJC) codes

  • Modelling and Simulation
  • Statistics, Probability and Uncertainty
  • Control and Optimization
  • Applied Mathematics


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