Machine Learning Grey Model for Prediction

Grey model GM(1,1) which implies first-order derivative and one independent variable is considered to predict results producing better accuracy. Solving the grey model GM(n,m) for prediction is still being a challenge due to erroneous results of higher order derivatives, where n is the derivative order and m, number of independent variables. In this paper, we solve this challenge through reduction method and recursive computation. Thereupon, we propose a new method called GM(n,m,k) which is generalized using Machine Learning, where k is a set of constraints belonging to m dependent or independent variables of a given system. We have applied and tested the proposed grey model to applications in airlines industry and it is proved to give results with much better accuracy than the traditional grey model.