论文部分内容阅读
常规灰色模型主要适用于数据量少、波动幅度不太系统,短期预测时预测的精度较高。马尔科夫链适用于数据随机波动较大系统,可以对预测结果进行有效的修正。本文针对中国国际航线客运量(中国大陆出发)建立新陈代谢灰色马尔科夫预测模型,预测2014~2016年国际航线客运量,并将预测数据与实际数据进行比较,比较结果表明新陈代谢灰色马尔科夫预测模型比灰色模型和灰色马尔科夫链预测模型精准度更高,具有较强的实用性。
The conventional gray model is mainly suitable for small amount of data, the fluctuation range is not too systematic, and the prediction precision in short-term prediction is high. Markov chain for large random fluctuations in data systems, the prediction results can be effectively amended. In this paper, we establish a metabolic gray Markov forecasting model for the passenger volume of Chinese international routes (from mainland China), predict the passenger volume of international routes from 2014 to 2016, and compare the forecast data with the actual data. The results show that the metabolic gray Markov forecast The model is more accurate than the gray model and the gray Markov chain prediction model and has strong practicability.