A Gray Model With a Time Varying Weighted Generating Operator

Abstract

A gray model with a time varying weighted generating operator is put forward in order to fully extract information concealed in recent data. This model increases the weight of new data and reduces the influence of some possible data fluctuation. The relationship between the sample size and the error from the inverse time varying weighted generating operator is discussed. Compared with traditional gray forecasting models, the results of the practical numerical examples demonstrate that this new model performs well in forecasting problems with limited data, and provides reliable and acceptable accuracy for future prediction.

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