A high precision global prediction approach based on local prediction approaches
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A grey-based rough approximation model for interval data processing
Information Sciences: an International Journal
Real formal concept analysis based on grey-rough set theory
Knowledge-Based Systems
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In this paper, we propose a hybrid grey-based dynamic model, then it is applied to the prediction problem of international airlines amount increase in China. The work is motivated by the following observations: First, a system of international airlines is an uncertain dynamic system, and the effects of other systems on the system being monitored are also unclear. Thus it is difficult for us to predict next annual airlines amount from the system. Second, grey system theory is one of the methods that used to study uncertainty, and it is superior in mathematical analysis of systems with uncertain information. The system of international airlines can be viewed a grey dynamic system, therefore grey dynamic model GM(1,1) which is a single variable first order differential prediction model based on grey system theory can be used to solve the prediction problem. Third, since the development trend of international airlines amount is affected by variant random factors, it is difficult to obtain high predicted accuracy by single grey dynamic model. The work procedure is shown as follows briefly: First, the Markov-chain is integrated into GM(1,1) to enhance the predicted accuracy. Second, we present Taylor approximation method based on grey interval analysis for obtaining high accuracy furthermore. Finally, the statistics data of international airlines amount from 1985 to 2003 in China is used to verify the effectiveness of proposed model.