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

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 analysis and prediction based on accumulated generating operation (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 information 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 approximation method to optimize the whitening coefficient and made predicted error reduce to minimum. Finally, We presented three examples to verify the effectiveness of proposed model.