Non-Equidistance Grey Model Based on Grey Interval Weighting Accumulated Generating Operation

Abstract- Non-equidistance grey model NGM(1,1) which was proposed by Deng has emerged as a powerful tool for the prediction and analysis of non-equidistance data series under uncertain system. It carries out the system anal-ysis and prediction based on accumulated generating op-eration (AGO) to reduce the randomness of original series and obtain high accuracy. But the AGO only used observed whitening data, thus it disregards the existences of grey in-formation in the grey interval. Such that the accuracy of NGM(1,1) is not good and predicted value will bring large error. In this paper, we proposed a new NGM(1,1) based on grey interval weighting accumulated generating operation (GIWAGO) to enhance the predicted accuracy. The work procedure is shown as follows briefly: First, we presented a whitening coefficient based on grey interval to generate weighting accumulated series. Second, after the whitening coefficient was set initial value, we presented Taylor ap-proximation method to optimize the whitening coefficient and made predicted error reduce to minimum. Finally, We presented three examples to verify the effectiveness of pro-posed model