Abstract:

When dealing with prediction problems, it is often in the case that the original data are interval numbers with high random fluctuation. In order to improve the accuracy of interval grey number prediction, a grey Markov prediction model based on kernel and gray scale is proposed. This method builds a prediction model based on the interval grey number kernel sequence to realize the prediction of interval grey number kernel, and the upper and lower bounds of the grey number range are derived from the grey number kernel. On the premise of keeping the interval grey number independent and complete, the interval grey number prediction model is constructed, and the Markov prediction model is used to correct the prediction results. In the trend prediction of air cargo volume, this model shows that the result of Markov chain correction is an underestimation compared with the interval gray number prediction data. The results can help strengthen the macro understanding of the air cargo market and provide a reference for economic decision.

Key words: nuclear, gray, gray Markov prediction, air freight