  Grey system theory is an important mathematical tool for describing uncertain
information in the real world. It has been used to solve the uncertainty
problems specially caused by lack of information. As a novel theory, the theory
can deal with various fields and plays an important role in modeling the small
sample problems. But many modeling mechanisms of grey system need to be
answered, such as why grey accumulation can be successfully applied to grey
prediction model? What is the key role of grey accumulation? Some scholars have
already given answers to a certain extent. In this paper, we explain the role
from the perspective of complex networks. Further, we propose generalized
conformable accumulation and difference, and clarify its physical meaning in
the grey model. We use our newly proposed fractional accumulation and
difference to our generalized conformable fractional grey model, or GCFGM(1,1),
and employ practical cases to verify that GCFGM(1,1) has higher accuracy
compared to traditional models.
