Open AccessPrediction Of Arrival Volume Based On Improved Markov SCGM(1,1)c Model
Author(s) -
Mengying Fan,
Mingxia Zheng,
Hu Liu
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/688/4/044023
Subject(s) - markov chain , markov model , residual , markov process , computer science , algorithm , statistics , mathematics , machine learning
Aiming at the fact that the arrival gauges of port ships have the characteristics of increasing trend but abnormal fluctuation, and combined with the prediction methods commonly used for random events, this paper proposed a Markov SCGM(1,1)c model for a single-factor system, and the Markov SCGM(1,1)c model is improved by using the method of equal dimension gray number and sliding transfer probability matrix. The results show that the residual mean of the improved Markov SCGM(1,1)c prediction model is reduced to a certain extent, that is, the fitting degree is improved, the prediction accuracy is improved, and the ability to modify the mutation data is enhanced, which meets the actual requirements and provides theoretical support for port anchorage planning.