The Discrete Grey Power Model Based on the New Information Priority and its Application

Abstract

How to upgrade the performance and suitability of the forecasting method and reduce the modelling error for nonlinear small sample data series has been one of the most key problems in grey system theoretical exploration. The main work of this study, a discrete grey power model is formulated that can not only simulate the development of exponential and power function systems but can also reflect the interaction between them. The new model not only ensures that the more recent information is given some priority but exhibits a lower simulation error and greater prediction accuracy. The findings indicated that the presented model minimizes the average relative error in theory, with better adaptability than previous models in simulation and prediction. The case study demonstrates that the new model is more appropriate for predicting small sample data than the previous grey power model. This research addresses limitations in existing grey models and expands the grey model system.