摘要
灰色模型不适合长期、随机性和波动性较大的数据序列预测,针对这一问题引入马尔可夫预测理论,建立了公路运输弹性系数的灰色马尔可夫预测模型。模型兼有灰色预测和马尔可夫预测的优点,克服了随机波动性数据对公路运输弹性系数预测精度的影响,拓宽了灰色预测模型的应用范围。通过湖南省公路客运弹性系数预测的实例证明,基于灰色马尔可夫的公路运输弹性系数预测模型的预测精度普遍高于灰色GM(1,1)模型的预测精度,具有较好的实用性。
The grey GM （ 1,1 ） forecasting model is not suitable for the prediction of highway transportation elasticity coefficient data, which are of long period, random and big volatility characters. So, the paper presents a new model to optimize the outcome of the forecasting based on Markov prediction theory. And grey Markov prediction model used in the highway transportation elasticity coefficient is developed. The new models given in this paper can effectively overcome the impact of random fluctuating data on the forecasting accuracy for highway transport elasticity coefficient, and it can broaden the scope of application of grey forecasting model as well. The practical case in Hunan Province shows that the forecasting accuracy obtained from the approach given in this paper is more significantly superior than that attained from GM（ 1,1 ） model.
出处
《西华大学学报（自然科学版）》
CAS
2009年第4期26-29,共4页
Journal of Xihua University:Natural Science Edition
基金
国家863高技术研究发展计划(2007AA11Z213)
校内青年科学基金项目资助清单(2006005)
关键词
弹性系数
公路运输
灰色模型
马尔可夫模型
预测
elasticity coefficient
highway transportation
grey model
markov model
prediction
