An extended grey forecasting model for omnidirectional forecasting considering data gap difference

Der Chiang Li, Che Jung Chang, Wen Chih Chen, Chien Chih Chen

Research output: Contribution to journalArticlepeer-review

55 Citations (Scopus)


To achieve effective and efficient decision making in a highly competitive business environment, an enterprise must have an appropriate forecasting technique that can meet the requirements of both timeliness and accuracy. Accordingly, in the early stages, building a forecasting model with incomplete information and limited samples is very important to a business. Grey system theory is one of the prediction methods that can be built with a small sample and yet has a strong ability to make short-term predictions. The purpose of this study is to come up with an improved forecasting model based on the concept of this theory to enlarge the applicability of the grey forecasting model in various situations. By extending the data transforming approach, this method generalizes a building procedure for the grey model to grasp the data outline and information trend. Specifically, a novel inverse accumulating generation operator is developed to enable omnidirectional forecasting. The research utilizes observations of the titanium alloy fatigue limit along with temperature changes as raw data to verify the performance of the proposed method. The experimental results show that not only can this method expand the application scope of the grey forecasting model, but also improve its forecasting accuracy.

Original languageEnglish
Pages (from-to)5051-5058
Number of pages8
JournalApplied Mathematical Modelling
Issue number10
Publication statusPublished - 2011 Oct

All Science Journal Classification (ASJC) codes

  • Modelling and Simulation
  • Applied Mathematics


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